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Nelson Liu's Blog

Student perspectives on applying to nlp phd programs.

This post was written by : Akari Asai , John Hewitt , Sidd Karamcheti , Kalpesh Krishna , Nelson Liu , Roma Patel , and Nicholas Tomlin .

Thanks to our amazing survey respondents : Akari Asai , Aishwarya Kamath , Sidd Karamcheti , Kalpesh Krishna , Lucy Li , Kevin Lin , Nelson Liu , Sabrina Mielke , Roma Patel , Nicholas Tomlin , Eric Wallace , and Michihiro Yasunaga .

This post offers and summarizes student advice and perspectives on the NLP PhD application process, with a focus on programs in the US. We asked twelve recently-successful NLP PhD applicants a range of questions about the application process—this post compiles the broader themes and advice that run through the majority of responses. Make sure to check out the complete set of responses ! A tarball is also available for those who cannot access Google Drive .

⚠️ Disclaimer ⚠️: While we’ve all gone through the application process and have thoughts to share, we aren’t experts or authorities on this (highly random) process. Our advice comes from our unique perspectives and backgrounds, and not everything will generalize. That said, we hope that the differences and similarities in our shared experiences will be useful to consider.

Professors have also written advice to applicants from their side of the process, see Kalpesh Krishna’s compilation of graduate school application advice .

Table of Contents

Pre-application, statement of purpose, letters of recommendation, publications, transcripts / grades, standardized exams: gre / toefl, interviews / post-application calls, deciding where to go, misc. topics, in conclusion.

Deciding to apply at all is not an easy choice, and several respondents took additional time, either in school or in industry, to explore new fields and become more certain that pursuing a PhD was the right decision for them. Choosing where to apply is also an involved process, and involves trade-offs between factors like research area fit, location, and (perceived) selectivity. This section explores this preliminary part of the application process, along with useful insights from applicants on different aspects of this decision.

A lot of the perspectives in this post are aimed towards people already seriously considering a PhD—for instance, seniors or MS students. If you are a student considering a PhD, but still have a significant amount of time before you apply, John Hewitt’s blog post contains useful insights and advice on how to make the most of your time in school. In addition, Kalpesh Krishna’s extensive compilation of application advice might yield things to keep in mind through the years.

Why apply now?

For many of the respondents, starting a PhD was the natural “next step”—they were in the final year of their undergraduate or masters degrees, and had spent enough time doing research to realize that a PhD was worth the opportunity cost to them.

While I did not have any *ACL papers while applying...My goal was to get into a good PhD program and start doing research full-time (which is why I was applying to a PhD program in the first place) rather than get into the very best PhD program. – Kalpesh Krishna

Waiting to apply also has clear benefits—many respondents felt that they would be stronger applicants after an additional year of research experience (and the associated publications and stronger letters of recommendation that might come with it).

“The year away from academia gave me the clarity on how much I really wanted to do a PhD and how much I love academic life. In this year I used my free time to explore interesting research directions and collaborated with friends. It made me realise that I enjoy research and to be able to do it for a living would be just perfect.” – Aishwarya Kamath

“I was also unsure at that time what kinds of directions I wanted to go in or if I even wanted to commit so many years of my life to additional school...By the time fall 2018 came around, I’d done a full year of thinking and growing my research skills, so I felt a lot better about diving into the process.” – Lucy Li

Several people found value in waiting because it gave them the time to reflect on their next steps. For instance, Lucy and Aishwarya used the time to further develop their research interests and think about what areas were exciting to them. In particular, Aishwarya spent a year in industry, which made her realize what she was missing an academic setting and drove her to apply and return.

On the other hand, several also offered caution about waiting with the sole intention of improving your profile. As PhD applications get more and more competitive each year, more papers or experience doesn’t necessarily mean a stronger application since things are inherently relative. Several agreed that having publications at top conferences is not a necessary component of a strong application, especially if one has relatively limited research experience (e.g., applicants from undergrad) or has strong recommendation letters. A recent blog post about the machine learning PhD application process investigates admission statistics at one of the top schools (Fall 2018), and shows admission is not determined solely on publication records, but depends on the other factors, especially applicants’ background and letters of recommendations.

For instance, Kalpesh and Akari considered waiting a year since they did not have any top-tier NLP publications at the time, noting that:

• Things get more and more competitive each year, so more papers doesn’t necessarily mean a stronger application since things are inherently relative.
• Applicants with master's degrees are expected to have more publications and experience than undergraduate applicants.
• There is a large amount of uncertainty involved in research / writing papers, so things are not always going to pan out for reasons out of your control.
• They thought that they were still reasonably strong applicants for many of the places they were applying to.

Kevin and Akari also mention that, if you have the resources, you can apply multiple times.

If what you really want to do is to immediately get into a grad school and continue doing work that you are excited about, you should apply. – Roma Patel

Choosing where to apply

When choosing where to apply, the majority of respondents focused on a few factors:

• Overwhelmingly, the strongest factor for everyone was faculty : finding schools with professors that you’d want to work with, and with a strong presence in allied fields. Several mentioned applying to places only if there were 2 or more relevant faculty.
• Location was also a key factor for many: finding schools in places that you think you’d be happy living in for 5+ years.
• Lastly, many also considered proximity to industry connections / possible external collaborators .

Some also took the relative prestige of a school into account, with the thinking that prestigious schools attract strong peers, which means that you can learn more and work with amazing people.

There’s also a case to be made for applying to a mixture of (1) programs that you’re relatively confident you can be admitted to and (2) “top choice” programs that might have a bit more randomness in the admissions process (of course, all the schools you apply to should be places you’d be happy going to). However, it’s easy to be a bit too conservative when choosing where to apply—remember that you only really need 1 offer. The majority of respondents applied to between 8 and 13 schools, though almost everyone was happy with the number of applications they submitted (Kevin, who applied to 4, thought it would have been helpful to apply to more).

NLP applicants in particular are lucky—there are amazing faculty scattered around the world in a variety of different environments. Start with a large list before filtering down, and focus on finding the right fit for you personally.

Talking to Faculty Beforehand?

I did not email faculty beforehand - I don’t think this helps (and in the case of a poorly crafted email, could actually hurt!). – Sidd Karamcheti

The majority of students did not email faculty before applying. Some faculty ask students to reach out—this will usually be explicitly mentioned on their webpage. In the absence of such a notice, a reasonable policy is to not send an email.

But that said, if you are in the vicinity of a school or doing an academic visit -- feel free to reach out to the faculty there and ask if they have a half-hour slot to meet! – Roma Patel

I emailed one prospective advisor and asked to meet at a conference. In general, I think this is a good strategy, especially if you have research-related things to talk about with them. (Which hopefully you will, if they’re a good advisor fit!) – Nicholas Tomlin

Several respondents were fortunate to meet potential future advisors at workshops or conferences / if they happened to be in the area, and found them to be quite receptive to short research meetings. It’s good to go into these meetings with a sense of (1) what you’d like to get out of it, and how to use this meeting effectively, (2) an awareness of their recent work, (3) a mental list of questions that you think have informative or interesting answers.

...one of my undergrad advisors emailed a couple prospective grad advisors on my behalf, and asked them to look out for my application. I think this was particularly helpful and is maybe something worth mentioning to your undergraduate advisor. – Nicholas Tomlin

It is appropriate to selectively ignore advice about cold-emailing— Prof. Yonatan Bisk has a great guide that walks through the why, when, and how .

Back to the top.

The statement of purpose is an opportunity for you to convey what you’ve worked on and what you’re interested in. Above all, make sure the statement is genuine and uniquely you. The “accept/reject” dichotomy of applications might make this process seem like a game—leading many to believe that it’s better to win the game (that is, be accepted) than to lose. While it’s tempting to shape each application to say what you think faculty might want to hear, being yourself will lead to the best outcome in the end. Remember that programs and students are both looking for the right fit—the statement is a fantastic opportunity for both sides to assess this.

If your statement is genuine and makes clear why you want a PhD, it will resonate with the people you want it to resonate with. – Sabrina Mielke

Timeline: When to Start and Finish Writing

With respect to starting writing, it is sometimes good to leave it late enough to wrap up any ongoing research projects at the end of the summer so you can write concrete things about them. For finishing writing, it’s good to have a near-ready draft at least a month before. – Roma Patel

Try to set aside a fixed period of time to work on your statement. While starting earlier rather than later is usually better, try to start writing a draft once you think your current projects and interests are concrete enough to write something substantive. Strive to have a preliminary draft that you’re happy with at least a month before the deadline. You can then send this to your advisors for feedback; continue editing and iterating until the deadline and/or you’re happy with how things look.

Structuring a Statement of Purpose

The goal of the statement is to talk about your past (research) experience, and how that has prepared you for a career in research (why you’re qualified for grad school). – Sidd Karamcheti

Your statement of purpose should uniquely describe your research experience and elaborate on the process you went through as you undertook your first few research projects. Give enough detail about your past work to allow them to assess the value of the work and also to concretely show that you knew what you were doing at every step of the process. Then fold this into your research as a whole. Try to leverage insights from both the actual work as well the experience of doing research, to formulate how you would undertake future projects during your graduate school career.

Many professors do tell you what they’re looking for in a SoP (JHU CLSP for example has hints at https://www.clsp.jhu.edu/apply-for-phd/phd-admissions-faq/ ), so do use that resource. – Sabrina Mielke

Tailoring Each Statement for Specific Universities

I only tweaked the final paragraph. In this paragraph, I specifically mentioned 2--4 faculty that I wanted to work with and provided a one sentence rationale. – Eric Wallace

Our survey respondents were quite divided on this question. A few respondents significantly tweaked their statements for each university to reflect the subset of their interests relevant to the prospective advisor’s research. However most respondents kept 80-90% of their statement identical and only modified the last 1-2 paragraphs with university specific information - such as the names of the professor they were interested in working with. Most agreed that it is good to have at least some university-specific information to form a connection between your own research goals and a prospective advisor’s research directions.

It is good to have concrete reasons laid out in your statement as to why you want to go to this school and work with these faculty on interesting problems. So definitely tweak the section of your statement that stresses on this. – Roma Patel

Getting Feedback on Your Statement

Your recommenders will get a better sense of your research interests so it can help them write your recommendation and they have also been through similar processes. – Kevin Lin

It is good to have a near-complete draft of your statement ready in time to send to your recommenders before they begin to write your letter of recommendation. There are multiple benefits to this. Reading your statement will help them better understand your research interests, which will not only allow them to concretely write things about you in their letter, but might also bring up useful pieces of advice from them based on what they know of the people working in that research area. They will also usually give you feedback on the overall statement—they have possibly read countless statements over the course of their career and will be able to fairly judge and evaluate this in context. Your research advisors and recommenders are likely both extremely knowledgeable and also have your best interests at heart, so remember to ask for feedback and advice on your application!

Using this as a Learning Opportunity

In my statement, I mostly talked about my past experiences and how they feed into my current research interests. I tried to paint a picture that enables the reader to better understand how I reached / why I do the research I do. – Nelson Liu

Write out your journey as a researcher from the beginning to the present. This will convey important information about you and your research, which can be illuminating for both your reader and for yourself. Chances are that you will write dozens of similar statements in the future, whether they are research statements for fellowships, project proposals, or grant applications. Use this as a learning experience! Writing your statement of purpose is not only good practice for the future, but also a rare invitation to reflect upon your interests and motivations.

Letters of recommendation are often cited as the most important part of a PhD application. In our survey, every respondent marked letters as either the most or second-most important component. Given that the admissions committee is optimizing to admit candidates with a high likelihood of reliably producing excellent research, a letter from a fellow academic that effectively claims you’ve been able to do so is a strong signal that you’re a good candidate.

What to look for when choosing letter writers

Your letter writers should be people who know you well enough to speak about your skills and your strengths as a PhD candidate ... people you have worked with who are doing relevant research in the field and people you have genuinely been advisors to you… – Roma Patel

It can be helpful to view letter writers as your primary advocates in the admissions process. They want their excellent undergraduate students or research assistants to succeed, and they’re singing your praises in order to argue for your spot in graduate school. From this view, it may be clear that they should know you, your strengths, and your goals. Of course, some of your letter writers will know you better than others, but each should be able to at least advocate for your excellence in how you worked or interacted with them.

There’s often a tradeoff between (1) how well you know the letter writer, (2) how cool the work you did with them was, and (3) how well-known they are. As a first approximation, attempt to have all 3 letter writers know you through some kind of research collaboration. Simply doing well in their class, or TAing for them does not necessarily make for a strong letter. On the other hand, an industry researcher who can vouch for your research ability may be able to make a stronger statement. This brings us to (3) how well known the letter-writer is. Perhaps unfortunately, letters from well-known members of the field are (very) highly regarded. This may be due to fame bias—the professors on the application committee can rest assured that they know so-and-so from X university consistently recommends only excellent students. As suggested at the beginning of this paragraph, this will play some role in the tradeoff, but keep in mind that a famous professor who doesn’t really know you won’t write a strong letter.

Each of the components mentioned above—personal knowledge of you and your work, successful research and fame of the writer were mentioned by our respondents.

I chose professors with whom I had completed somewhat successful research, and who were likely to be known by my prospective advisors. For better or worse (probably worse), connections between letter writers and prospective advisors seem to matter a lot. – Nicholas Tomlin

When to start looking for recommenders

People get started in research at different times, but by the time of application, you need three people who can advocate for your spot in graduate school (though again, not all need to be equally strong or know you equally well). When should you start building these relationships? The easy answer is “as early as possible”. Research takes a long time, as does getting settled in a field and starting to make real progress. This creates a definite bias towards those who start research earlier and collaborate widely (3 professors means a lot of connections to make). However, everyone’s research story looks different, and no student should think it “too late” to go for a PhD (though a master’s and/or further years of research experience may be necessary.)

To back this up, note the wide range of times that our respondents started working with the people who would end up being their LoR writers.

Note that this histogram includes one data point for each letter writer for each respondent. (Not everyone mentioned all three writers, and one mentioned four.) I counted “summer before 3rd year” as “2nd year.” That’s a lot of letter writers from the third and fourth (!) years. Many respondents who met their letter writers after their third year did indicate that it would have been better to start earlier, but the data somewhat makes sense—as you progress through your studies, you gain more research experience.

Asking for specifics in your letter, and getting them submitted

Recall that your letter writers are your advocates—you should feel empowered to bring up all the awesome things that you did with them, and ask (but not demand) that they mention specific things. These requests may be to tailor their letters to your statement of purpose. Think that your efforts in conducting replicable science in a world of AI hype are awesome? Your letter writer may agree, but likely wouldn’t think to mention it if you don’t remind them.

I made sure to send a reminder email 2 weeks, then 1 week, then a few days before applications were due. – Nelson Liu

Likewise, remember that they’re human and busy, and very well may forget your letter if you don’t send them a few reminders. PhD applications tend to have lenient letter of recommendation deadlines but it’s better to keep on top of them with tastefully-spaced reminder emails—better to not test the waters in this context.

I think that having a published conference paper greatly increases your chances, but I think that papers are merely a signal for something more important: can you complete the full research process, from idea inception to experiment execution to writing things up? – Nelson Liu

Most respondents felt that publications are an important part of a strong application, but are not necessary if you have stellar recommendation letters talking about your research aptitude. Admission into PhD programs in computer science (especially at top schools) is quite competitive, and many candidates have publications, especially candidates applying after year-long research positions such as AI residency programs.

Publications are just tangible evidence - if you can show other evidence that you are able to do research, that you learned something, that you have skills/conclusions that you’ve taken away from the experience, then you should be fine. – Sidd Karamcheti

Publications are a good way to show concrete research output. This acts like “hard evidence” of research aptitude, which is the primary criterion used to judge PhD applicants. Alternative ways to show concrete research output could be excellent research code releases or insightful blog posts.

Almost all survey respondents thought that grades and GPA scores play only a minor role in NLP PhD admissions. It is wise to not stress too much about improving your GPA, especially if compromises the time spent doing research. Things might be different in more theoretical fields though, where coursework might be closer to research.

Take an intro to NLP course! Take machine learning or a specific linguistics course or anything else that clearly shows that you have studied the topics you are excited about in depth. – Roma Patel

Interesting classes off the beaten path may let you stand out from the crowd. – Sabrina Mielke

The choice of coursework typically acts like a skillset evaluation during PhD admissions, checking whether candidates are familiar with the fundamental techniques required to conduct their research. Coursework can also help present a coherent academic history when combined with the statement of purpose. Some courses might help an applicant stand out from the crowd, especially if they’re uniquely relevant or off the beaten path.

Sometimes, the exact preparation matters less than evidence that you’re capable of learning important background material. E.g., despite me not having strong probability/stats background, a few professors said they were impressed by my (completely irrelevant) pure math background. – Nicholas Tomlin

While coursework does not play a major role in admission decisions, many respondents mentioned that courses are a great way to learn the fundamentals and get interested in a particular field, often acting like a precursor to research.

I get the sense that the GRE doesn’t really matter unless you do abysmally. – Nelson Liu

Nearly everyone agreed that scores from required standardized tests are not deal-breaking as long as you meet a minimum threshold. Having a suspiciously low score could raise questions, but barring failing the exam, this should not significantly impact your entire application. That said, this is a required checkpoint on your application, so keep aside time to get this done correctly.

There is no glory or shame in taking too much or too little time, so it is better to not compare to others and keep aside the right (and possibly minimal) amount of time you think you need to prepare. – Roma Patel

Try to give yourself at least 1-2 weeks of study time before the actual test. Don’t consider the amount of time you see others spending on this — assess yourself and allocate larger amounts of time to topics that you are uncertain about and think could use the extra effort. Remember to review all the topics you need to, take a few practice tests, and then just take the exam and don’t stress about the score.

It is usually not worth the extra time, effort, cost (or effect) to redo the exam. So prepare well once, take the exam, and don’t stress about the score once you are done with it. For what it’s worth, future years will likely see this disregard and ambivalence towards scores on tests heightened — lots of schools have already removed the GRE requirement, while others have definite plans of doing so in the coming years.

In general, international students must submit their TOEFL (or IELTS) scores to demonstrate competency in the English language — however for some schools, international students who have received degrees in US schools or received their instruction in English do not need to submit TOEFL scores. Unlike in GRE, applicants MUST score higher than the minimum requirements if universities sets minimum scores. The minimum requirements vary from program to program. For example, the Cornell CS PhD program sets the minimum scores for each section (Listening 15, Writing 20, Reading 20, Speaking 22), while the MIT EECS PhD set the total minimum scores to 100. Make sure that you meet TOEFL scores before the application deadline. Unfortunately, the applicants whose TOEFL scores lower than the minimum are likely to be “desk-rejected”.

Interviews in USA are less formal - more general discussions about research interests. Interviews for Europe in my experience were more in depth, as they expect you to already have knowledge of your field (since you can only apply after a Masters), have a research plan and expect you to have already surveyed literature in your chosen field of interest. – Aishwarya Kamath

The interviews and visit days will differ significantly over the range of schools you’re considering—both in their intended purpose and in the amount of information you can glean about the school and faculty from this one interaction. Some schools do pre-acceptance visit days, with offers conditioned on the interviews and ensuing discussions. Others do virtual interviews over the phone or video calls. And of course, some schools choose not to conduct interviews.

While each interview experience is largely dependent on the candidate in question, most of our survey respondents agreed that these conversations follow the same general pattern.

The general format was like: “Tell me about a research project you worked on (pick one that is most exciting and introduce)”. The professor would ask some questions, like “why did you consider this model / run this experiment?”, “what is the conclusion?”, “what did you learn through this project?” “What is your research interest?”, “What are you interested in doing for your PhD (and your career)?” -- it’s good to think in both short term and long term “Do you have any questions?” -- you can ask any questions about the lab, like the culture, research goals, how advising/meeting works. – Michi Yasunaga

This is mostly a means of trying to get a sense of what you are like as a person and what your research interests are, to assess both compatibility and mutual interests. Your interviewers will generally ask you to talk about the research you have done — and will interrupt with questions about things that they are interested to hear more about. Overall, this is less of an assessment of your knowledge, rather than them getting insights into how you solve problems and talk about research.

I didn’t enjoy the whiteboard interview. – Nicholas Tomlin

This sometimes happens. If professors want to assess a specific component of your application, or want to know the extent of your knowledge about a certain topic, they will ask you technical questions that can range from explaining or solving an algorithm, writing out equations or explaining computational and implementation-specific aspects of things you have done. Most of our survey applicants however, did not have to go through this and their interviews largely consisted of general research conversations.

You should definitely know your own work inside-out, but don’t stress about having to know every intricate detail about every subfield in NLP. – Roma Patel

While it is not important (or even possible) to know everything little thing about every research area in NLP, you should be aware of work being done in areas related to you. Most importantly, if you have written about something in your statement, you should be able to confidently speak about it and answer any questions that they throw at you. Take time to look into every detail and ensure that you know the fundamentals of your work before your interview.

Remember that this is a two way street—while they’re assessing whether you’d be a good fit for their program, you should be probing whether this place / professor is a good match for you. – Nelson Liu

There is usually a part of the interview where the interviewer steps back and asks you to ask questions — use this time to probe at any uncertainties or lingering questions that you have. If you have questions about their previous work, thoughts about future possibilities, or even just general questions about the program or the department, use this time to clear any doubts and get all the answers you will need to make a decision.

if you don’t know something, it is okay to say that you don’t --- ask questions that help you understand it more and treat it as a learning experience. – Roma Patel

The only thing I will tell you not to do in an interview: pretend. Professors are good at spotting that kind of thing and they will strongly judge you for it. Just be honest and genuine. You are starting your PhD. You don’t need to know things -- just be willing to grow. – Sabrina Mielke

Also, don’t worry if you do not know everything the interviewers ask. Just try to be as honest and genuine as you can, and show that you are willing to learn and grow, instead of pretending to know the topics.

I think the interviews as an initial conversation really affected where I seriously considered—the places with interviews that I thought were more fair / reasonable gained legitimacy. In the best case, it was basically a research conversation with a senior researcher, and a great opportunity to get feedback / hear what they think about the field. Overall, I thought they were quite valuable, and I wish that I had treated them less as assessments and more as opportunities. – Nelson Liu

Make the most of your interviews! All applicants agreed that overall, the interviews were friendly and engaging experiences. Think of this as an opportunity to speak about and answer questions about your work and to have a mutually engaging research conversation.

One useful piece of advice from one of my undergrad advisors was to, “Talk about your research ideas! Remember that what most faculty really want is to be able to discuss the research that is important to them — and if you can do this and make exciting progress through these discussions, you will both mutually have a productive and happy career together.” – Roma Patel

If you’re fortunate to be considering multiple options, congratulations! It is a hard problem, but a good one to have—be aware of your privilege. The choice between graduate programs is an intensely personal one, and there are a variety of academic and non-academic factors to consider, all of which will influence your health, happiness, and productivity.

Something that people do not always remember when making a decision is that your advisor is possibly someone you will be talking to for upto 3 hours every week for nearly 6 years of your life. It is good to rethink whether or not you will be happy doing this with the faculty in question, if the two of you see eye-to-eye, can comfortably talk about both research-things and also life-things when they come up, and that they will encourage and help guide you in everything you need to do the research that is important to you during your PhD. – Roma Patel

In general, most respondents agreed that the most important factor is your primary advisor—who will you be working with during your PhD? Do you have mutual research interests? Are your communication and working styles compatible? Would you be comfortable talking to them about your struggles, both academic and non-academic? Do you have much to learn from them and their group? Do you feel supported by them? While it is hard to assess these deep questions before spending time to work with them, conversations and interactions during visit days will help you get a sense of whether things feel right. Trust your instinct—if things feel odd or unnatural, even during these initial conversations, you have plenty of reason to reconsider and be hesitant.

As an undergrad at a school with a large NLP community, I really benefited from having senior researchers around (e.g., grad students and postdocs)---I have so much to learn from them! I felt like I wanted to keep having such an environment in graduate school, which actually ended up being one of the defining factors in my final choice. – Nelson Liu

Many students also took note of the NLP community at every school they were considering. For instance, some prefer larger groups with many senior students and postdocs, while others prefer smaller, more-intimate groups. There are benefits and drawbacks to both sorts of research environments, and it ultimately boils down to personal preference and taste. It’s important that you feel like you have enough people around to talk about research and life—while your advisor is an important figure in the PhD, you will spend the majority of your time talking to and working alongside fellow students. Make sure that these are people that you’d love to be around for the next stage of your research career.

Sure, you’re picking a place to do research for the next 5+ years of your life, but you also need to be happy / have a life outside of research...I went climbing during a lot of my visits, mostly to assess convenience. – Nelson Liu

Another important factor to consider is the location. Several expressed weather / culture preference (mostly on the east-coast-vs-west-coast divide). Many also wanted to be in a place that was affordable for students and conveniently located to their favorite hobbies or recreational activities. While research fit is certainly important, you won’t be productive if you’re miserable—put your happiness and your health first, and make sure that you’ll be happy as both a student on-campus and as a resident of the area.

Prestigious schools attract strong peers, which means you can learn more and collaborate with amazing people. – Eric Wallace

Several also considered the relative “ranking” of a university or program (though this is almost impossible to objectively evaluate without implicitly considering the other factors). While rankings can tell part of the story, they’re not substitute for your own feelings and intuitions about where you belong.

At some schools, it was very clear who my advisors would be, while at others, it wouldn’t be decided until I’d enrolled. I preferred the former scenario since it involved less uncertainty. – Lucy Li

It’s also useful to consider the program’s requirements and logistics around advising. Are you guaranteed to be able to work with the advisor(s) you are interested in? Does the department have extensive qualification exams or requirements that might be hindrances to your productivity? Will you have to worry about funding?

Personal feelings actually do matter. If you feel (even slightly) uncomfortable, these negative feelings will grow during the five years. – Akari Asai

Once you have done an extensive comparison on all parameters (professional and personal), you might be stuck between 2-3 very good options. Try reweighting the parameters and see if the balance shifts towards one end. If you are still confused, don’t worry :) If it’s so confusing, both places are surely very good. You will need to work very hard wherever you go, and you won’t lose much choosing one over the other. Go with your heart. – Kalpesh Krishna

When it comes to the final decision, everyone agrees to go with your heart and feelings of what seems right to you. We’re all logical and analytical people (perhaps to a fault), but if you can’t make up your mind about where to go / are stuck between several options, pick the one that you feel the best about inside. One way to discern this: Suppose you’re picking between two places (this strategy generalizes to N). Take a coin, and assign one place to heads and another to tails. Tell yourself that the result of the coin flip will be where you end up going. Flip the coin, and observe the result. Are you relieved? Would you have preferred the other side? The answer to these questions might help you better understand how you really feel about the decision.

Whatever you do end up deciding, though, don’t regret it—the decision is done now, and you just have to put in the work to ensure that it is a good one. – Nelson Liu

Making the most of visit days

I didn’t end up going to most visit days -- which is not something that you should do. Go to every visit day! Talk to the other students visiting, the other students currently pursuing PhDs there and to the faculty there. Keep a list of standard questions about schools (requirements, professors, exams, time taken) and make a note of these for every school so that you have an easy way to compare at decision-making time. – Roma Patel

Many of our survey respondents recommend making the most of the visit days. Treasure this priceless opportunity to talk to professors (both in and outside of your field), meet PhD students, and get to know the other students in your cohort. As you continue your academic career, you’ll be seeing all of these people around in the future—get to know them now!

Talk to students most of all -- disturb them when they’re working to see what it’s like in the lab! – Sabrina Mielke

Before each visit, it’s useful to think a bit about what you’d like to get out of it. This might result in a list of questions you’d like to answer, or people that you’d like to talk to. Don’t be afraid to contact PhD students in the department and ask to meet; the majority are happy to do so, and would love to give you advice, hear about what you’re working on, and talk about their research. Talking to students is of the utmost importance; they will tell you what it’s really like in the department, and it’s useful for getting a sense of the overall department culture and graduate student community.

My advisor, in her infinite wisdom, gave me a useful piece of insight that had not struck me before. "What most people don't realise, is that the people that you are meeting and talking to over these visits will likely be in your life, for the rest of your life. Go to as many visits and talk to as many prospective students as you can — some of your closest friends and advisors will come out of these interactions." – Roma Patel

Residency Programs as Precursors to your PhD

I see a couple benefits of working in AI residency which I did at AI2. 1) if you aren’t sure if you want to do a PhD, this is a pretty good way to find out, and after the residency you will be in a reasonable position to pursue both industrial and PhD positions. 2) You will be exposed to a new set of people, and it is helpful to learn from different ways of doing research 3) I personally changed my research direction towards more NLP and this was a great way to explore different research topics and build up the skills I needed to pursue those topics. – Kevin Lin

Be really really clear why you’re doing the residency - the reason to do the residency/work is to do something you could not otherwise do at grad school/if you’re not sure about grad school. – Sidd Karamcheti

It’s really important to consider why you want to do a residency program. As our survey respondents mentioned, there are a few different paths that lead to residencies—foremost among them is if you’re not too sure about wanting to do a PhD, and you want some more research experience (working with a couple of different mentors with possibly different areas/interests than what you were exposed to as an undergraduate) before making a final decision.

Another reason a residency program is a good idea is if you’re sure about doing a PhD, but had limited exposure to different areas as an undergraduate. Especially if you’re considering PhD programs where you’re paired with an advisor/placed in a specific area outright, having a year to explore a bunch of different areas and work with different mentors with different styles will let you make a more informed decision. It’s totally possible that the residency program will introduce you to areas you would never have otherwise considered!

That being said, it’s worth noting that not all residency opportunities are created equal—several different companies are just in their first or second year of offering their residency programs, meaning that they’re subject to growing pains—without structured onboarding/tutorials you might spend a lot of time trying to figure out how to use company infrastructure, or you might spend a lot of time trying to figure out what different folks at the company are working on, and how research works in industry.

More importantly, you need to make sure your residency mentors are committed to the same goals that you are—a mismatch in expectations between you and your residency mentors is going to significantly sour your experience! If you want to explore a bunch of different sub-areas of your chosen research area, make sure your mentor is on board to try a few different projects over the course of the year! If you want to instead work on more long-term projects/existing initiatives at the company, make sure that your host is willing to connect you with these existing teams, and that there’s some structure in place that will let you (1) learn, and (2) contribute.

Finally, don’t feel like you need to do a residency to get the industry experience, or to explore different research areas. There is definitely a large amount of time you can spend exploring different areas in grad school, and you’ll have multiple summers to do internships where you’ll possibly get to work on projects very different from your core research agenda.

FWIW, you will likely intern at a lot of the places during the course of your PhD and will have a similar experience, so if the only reason you are considering a residency is because you think that is an experience you will never get at a later time --- this is likely not true. – Roma Patel

When submitting my application, I was pretty sure that I would defer for a year if I got an offer---there’s no rush, and the extra year might give me some interesting perspective. – Nelson Liu

If you’ve read this far, we hope that this discussion was useful. The admissions process is inherently stochastic, and there’s much that you can’t control—relax, have confidence in yourself, and goodluck!

Another good advice I received from my friend was “Don’t reject (by?) yourself”. I remember how uneasy and stressful I felt at the time of application, as I did not have strong publication records, and came from non top undergraduate schools in the US. Sometimes people value your unique back-ground, experience in other fields or find really positive signals in the letters of recommendation. Don’t hesitate to apply for good schools, because “I think I’m not good enough”. – Akari Asai

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Natural Language Processing

We rely on machines to understand human language and anticipate our instructions. Research in natural language processing seeks to build computers and autonomous systems that can understand and use human knowledge, primarily language and text. The goal of this research is to build intelligent systems that learn and communicate through language. Work in this area pushes the boundaries of artificial intelligence while also enabling advances in practical text processing applications that can have a broad impact on various real-world problems.

At Princeton, researchers develop novel algorithms, design new frameworks, and investigate theoretical foundations to tackle challenging problems in language understanding. Researchers draw on techniques like deep neural networks and reinforcement learning.

Associated Faculty

• Sanjeev Arora
• Adji Bousso Dieng
• Peter Henderson
• Karthik Narasimhan

Associated Graduate Students

• Adithya Bhaskar
• Howard Chen
• Ameet Deshpande
• Dan Friedman
• Vishvak Saivenkat Murahari
• Alexander Wettig
• Mengzhou Xia
• Zexuan Zhong

Doctoral Program

The Ph.D. program emphasizes rigorous theoretical work that has at its base a firm empirical foundation in language data. 

Students are provided with a broad-based background in linguistics, teaching experience in the classroom and other forums, and opportunities for original and high-quality research.  Our Ph.D. students write dissertations on a wide range of topics spanning and bridging many subareas of the field.  See our Ph.D. Alumni  page for dissertation titles and job placement information.

Overview of the Program

Through the completion of advanced coursework and strong methodological and analytical training, the  Ph.D. program prepares students to make original contributions to knowledge in linguistics, to articulate the results of their work, and to demonstrate its significance to linguistics and related fields.  At every stage in the program, students are encouraged to present and publish their research and to develop active professional profiles. 

Students generally complete the program in five years

• Coursework in core areas of linguistics, chosen by each student in consultation with faculty advisors to build the foundation that best suits their interests and goals.
• Fall Quarter: Includes seminar to introduce students to the research of faculty in the department
• Winter Quarter: Includes participation in small research groups or in one-on-one apprenticeships
• Spring Quarter: Includes beginning to work on the first of 2 qualifying research papers

Years 2 and 3

• Balance shifts from coursework to development of research skills
• Students complete two qualifying papers and then selects a principal advisor and committee for their dissertation by the end of year 3.

Years 4 and 5

• Devoted to dissertation and advanced research

Teaching Experience

As they move through the Ph.D. program, students also gain teaching experience by serving as teaching assistants in their second, third, and fourth year of graduate study. They also have access to the many programs provided by Stanford's Vice Provost for  Teaching and Learning , including the varied resources of the Teaching Commons .

Offers of admission to the Linguistics P.h.D program include funding for the full five years of doctoral study, including tuition and stipend, regardless of citizenship. 

We also encourage our applicants to apply for as many external fellowships and scholarships as they are eligible for; a compilation of funding opportunities for Linguistics graduate students can be found on our  Fellowship and Funding Information page .  Applicants should note that the deadlines for these fellowships are typically in the fall of the year prior to admission.

In addition, the  Knight-Hennessy Scholars  program is designed to build a multidisciplinary community of Stanford graduate students dedicated to finding creative solutions to the world's greatest challenges. The program awards up to 100 high-achieving students every year with full funding to pursue a graduate education at Stanford, including the Ph.D. degree in Linguistics. 

Additional information is available about the student budget , Stanford graduate fellowships , and other support programs .

Outside the classroom, there are many opportunities, both formal and informal, for the discussion of linguistic issues and ongoing research, including colloquia, workshops, and reading groups.

Partnership Opportunities

Although not part of the formal doctoral program, there are numerous opportunities for research and development work at the Center for the Study of Language and Information and  off-campus at local companies.  

Admissions Information

Natural Language Processing

The Natural Language Processing (NLP) Group at KCL is comprised of PhD and postdoctoral students, professors and others who are interested in solving computational problems related to the understanding of human language. This encompasses a wide range of topics including sentiment analysis, topic/event extraction, question answering, cross-modal retrieval, text illustration, social media analysis and many more, typically approached with machine learning.

All images have been generated using DALL-E.

Related departments

• Department of Informatics
• Faculty of Natural, Mathematical & Engineering Sciences

Ph.D. Programs

The Department of Linguistics offers four concentrations leading to the Doctor of Philosophy (Ph.D.) degree in Linguistics (see list below). No matter the concentration, our faculty work closely with students, guiding their research and supporting their passions.

• Applied Linguistics
• Computational Linguistics
• Sociolinguistics
• Theoretical Linguistics

Applicants to the Ph.D. program are encouraged to identify prospective research advisors, at least one of whom should be in the concentration to which they apply.

After entering the program, Ph.D. students may elect to add a minor in a second one of these concentrations [new policy effective Spring 2023].

An interdisciplinary (second) concentration in Cognitive Science is also available to Ph.D. students.

Master’s in Passing

If, in their course of the Ph.D. program, a doctoral student meets all of the requirements of a M.S. degree in Linguistics, he or she may apply to receive a “Master’s in Passing.” Please consult section IV.D.3 of the Graduate School Bulletin for full details about the “in passing” or “terminal” Master’s degree.

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2 PhD positions in NLP and ML for healthcare

UMCCountryNetherlandsCityAmsterdamPostal Code1105AZStreetMeibergdreef 9Geofield Where to apply Website https://www.academictransfer.com/en/338330/2- phd -positions-in- nlp -and-ml-for-he… Contact City Amsterdam Zuidoost Website https

PhD Student Position in Computer Science with a Focus on NLP and LLM in Musculoskeletal Medicine

31.01.2024, Wissenschaftliches Personal This position offers an exciting opportunity to engage in a PhD program at the Technical University of Munich, focusing on Natural Language Processing ( NLP

Research Fellow (Computer Science)

support Smart Nation applications Key Responsibilities: Design and implement natural language processing ( NLP ) algorithms and models for semantic analysis, e.g., topic modeling, embedding, named-entity

Natural Language Processing Engineer (Thai Language Specialist) for AI Singapore (Products)

development and engineering work for NLP (Natural Language Processing) products under AI Singapore. To complement the current NLP Hub team, we intend to hire an experienced NLP engineer with expertise in

Language Model-Grounded User Simulation for Personalised Conversational Systems

that the Department of Computer Science is a vibrant and progressive place to undertake research. The successful candidate will join the Natural Language Processing ( NLP ) group, one of the largest (approximately 30 PhD

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staff position within a Research Infrastructure? No Offer Description You will join the "CaRe- NLP : Human-Centric and Responsible NLP methods for Dutch healthcare" project. CaRe- NLP's main goal is to

UCD is recruiting a Postdoctoral researcher to implement natural language processing ( NLP ) tools to analyse interview data

College Dublin is currently recruiting a post-doctoral researcher to implement natural language processing ( NLP ) tools to analyse interview data. Fixed term contract for 2 years with a proposed start date

PhD Studentship: Language Model-Grounded User Simulation for Personalised Conversational Systems

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Computer Science: Fully Funded EPSRC DTP PhD Scholarship: Real-Time Spoken Language Understanding for Social Care Robots

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PhD Position F/M Multimodal Speech Analysis for Early Detection of Crohn's

facial expressions and modifications in speech patterns using NLP and machine learning techniques. This will be achieved by analyzing video recordings of multiple patients, with each recording annotated by

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Fully funded four-year PhD studentships in Natural Language Processing at University of Edinburgh

October 21, 2021

UKRI CENTRE FOR DOCTORAL TRAINING IN NATURAL LANGUAGE PROCESSING

Based at the University of Edinburgh: in conjunction with the School of Informatics and School of Philosophy, Psychology and Language Sciences.

Deadlines :

·       Non UK :     26 th  November 2021 ·       UK :            28 th  January 2022

Applications are now sought for the UKRI CDT in NLP’s penultimate cohort of students, which will start in September 2022.

The CDT in NLP offers unique, tailored doctoral training comprising both taught courses and a doctoral dissertation over four years. 

Each student will take a set of courses designed to complement their existing expertise and give them an interdisciplinary perspective on NLP.  

The studentships are fully funded for the four years and come with a generous allowance for travel, equipment and research costs.

The CDT brings together researchers in NLP, speech, linguistics, cognitive science, machine learning and design informatics from across the University of Edinburgh.   Students will be supervised by a world-class faculty comprising almost 60 supervisors and will benefit from cutting edge computing and experimental facilities, including a large GPU cluster and eye-tracking, speech, virtual reality and visualisation labs. 

The CDT involves a number of industrial partners, including Amazon, Facebook, Huawei, Microsoft, Naver, Toshiba, and the BBC.  Links also exist with the Alan Turing Institute and the Bayes Centre.

A wide range of research topics fall within the remit of the CDT:

·       Natural language processing and computational linguistics

·       Speech technology

·       Dialogue, multimodal interaction, language and vision

·       Information retrieval and visualization, computational social science

·       Computational models of human cognition and behaviour, including language and speech processing 

·       Human-Computer interaction, design informatics, assistive and educational technology 

·       Psycholinguistics, language acquisition, language evolution, language variation and change

·       Linguistic foundations of language and speech processing.

The next cohort of CDT students will start in September 2022 with applications being invited now.  Around 12 studentships are available, covering maintenance at the UKRI rate (currently £15,609 per year) plus tuition fees.  

Studentships are open to all nationalities and we are particularly keen to receive applications from women, minority groups and members of other groups that are underrepresented in technology.  Applicants in possession of other funding scholarships or industry funding are also welcome to apply – please provide details of your funding source on your application.

Applicants should have an undergraduate or master’s degree in computer science, linguistics, cognitive science, AI, or a related discipline; or have a breadth of relevant experience in industry/academia/public sector, etc.      

Further details, including the application procedure, can be found at:  https://edin.ac/cdt-in-nlp

Application Deadlines

Early application is encouraged but completed applications must be received  at the latest  by:

26 th  November 2021 (non UK applicants) or 28th January 2022 (UK applicants).

Please direct any enquiries to the CDT admissions team at:  [email protected] .

CDT in NLP Virtual Open Day

Find out more about the programme by attending the PG Virtual Open Week 9 th  November 2021, 2-3pm.  Click here to register:  Computing, Data Science & Informatics | The University of Edinburgh

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Department of Linguistics

Ph.d. program.

The main components of the Linguistics Ph.D. program are as follows:

• Course Requirements
• Language Requirement
• Generals Papers
• Dissertation
• Extra Funding Availability

All requirements, including two generals papers, should ideally be completed by the end of the third year, but in no case later than the end of the fourth. The dissertation prospectus is due on October 15 of the fall term of the fourth year. Failure to meet program requirements in a timely fashion may result in termination of candidacy. 

First-year students are advised by the Director of Graduate Studies (DGS) until they select a major field from the regular departmental faculty. Thereafter, progress toward completion of the Ph.D. requirements continues to be monitored by the DGS, but primary responsibility for overseeing study shifts to the major advisor. Students are free to change their major advisor at any time. By the end of the second year they should also select a co-advisor, who serves as a secondary advisor and faculty mentor.

Harvard Linguistics Graduate Student Handbook

Progress to the Degree (updated 7/1/2015)

A B+ average must be maintained in each year of graduate study. Grades below B- cannot be counted toward departmental requirements; two grades below B- in required courses will result in termination of candidacy. Ordinarily, a grade of Incomplete can only be converted into a letter grade if the work is made up before the end of the following term. No grade of Incomplete can be used to satisfy a departmental requirement.   No two programs of study are alike, but students should typically plan to complete the requirements for the degree according to the timetable below. Departures from this schedule must be approved by the main advisor and the Director of Graduate Studies.   Years G1 and G2: Course requirements are satisfied. By the end of the G2 year, the first generals paper should be well underway.   Year G3: Teaching duties begin. The first generals paper should be defended before the end of the fall term, and the second generals paper by the end of the spring term.   Year G4: Teaching duties continue. A thesis prospectus, naming a dissertation committee, is due on October 15 of the fall term; the committee must be chaired or co-chaired by a member of the Department of Linguistics and must include at least two members of the Faculty of Arts and Sciences. Dissertation Completion Fellowship applications are due at midyear.   Year G5: The thesis is completed and defended in the spring.

A.M. Degree  (updated 7/1/2015)

Graduate students who have completed two years of residence, who have fulfilled all the course requirements and language requirements for the Ph.D., and who have successfully defended one Generals paper, are eligible to petition for a Master’s (A.M.) degree.  

Note that there is no master’s program in Linguistics.                     

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• Department of Swedish, Multilingualism, Language Technology
• Doctoral Studies

Natural language processing

Third-cycle programmes in natural language processing provide in-depth methodological and theoretical knowledge of the multidisciplinary research area of language technology.

The focus of this subject is on the development and use of linguistic resources and language-technical tools for the resolution of research questions related to language technology and other disciplines. The programme provides fundamental knowledge of existing such resources and good conditions for the development of new resources, as well as solid experience of addressing language-technological research assignments with a basis in the linguistic resources required for their resolution.

The skills provided by the research programme in natural language processing are becoming increasingly important in the modern information society. They are also increasingly in demand within academia, including in areas other than language technology. The knowledge is applicable within areas such as information searching and other processing of information and texts, such as automatic translation services or search engines . Other examples of important areas of application include machine translation, computer-assisted language learning and corpus linguistics.

A PhD is a prerequisite for a lectureship at a higher education institution.

Supervisors in Natural language processing

The supervisors guides the doctoral student through the doctoral program, both in terms of PhD courses but above all in terms of writing the thesis. The following supervisor in the field of Natural language processing is available:

• Yvonne Adesam

Aleksandrs Berdicevskis

Gerlof Bouma

Dana Dannélls

Markus Forsberg

Dimitrios Kokkinakis

Peter Ljunglöf

Nina Tahmasebi

Shafqat Mumtaz Virk

Elena Volodina

Niklas Zechner

Our group is part of the UCL Computer Science department , affiliated with CSML and based at 90, High Holborn, London. We also organise the South England Natural Language Processing Meetup . If you are interested in doing a PhD with us, please have a look at these instructions . We also host a weekly reading group, you can find more details here .

Our paper, What the DAAM: Interpreting Stable Diffusion Using Cross Attention , has won a Best Paper Award at ACL 2023! Congrats to authors Linqing and Pontus!

Yihong will be presenting ReFactorGNNs in ELLIS PhD Symposium 2022 . Come to our poster if you are curious about why factorisation-based models are special message-passing GNNs!

Our paper ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective has been accepted by NeurIPS 2022! Congrats Yihong , Pushkar , Luca , Pasquale , Pontus and Sebastian !

Our work Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity has been selected as an outstanding paper at ACL 2022 !

The call for participation for the Shared Task at the DADC Workshop co-located with NAACL ‘22 in Seattle is now live! We have three fantastic tracks for you to participate in. Sign up here !

Additional resources from our work on Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation at EMNLP 2021 are now available! We are releasing a collection of synthetically-generated adversarial QA pairs and related resources as well as the models used to generate the questions .

Our AAAI 2022 tutorial , On Explainable AI: From Theory to Motivation, Industrial Applications, XAI Coding & Engineering Practices , was an outstanding success, with more than 600 attendees – check it out ! Congratulations Pasquale and collaborators!

News Archive →

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Sebastian Riedel

Honorary Professor

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David Adelani

Senior Research Fellow

Oana-Maria Camburu

Oana works primarily on explainable neural networks, building models that generate human-like explanations for their predictions.

Research Fellow

Eduardo Sánchez

PhD Student

Eduardo is interested in low-resource languages.

Jiayi is interested in Multilingual LLMs, Machine Translation, and Human-Computer Interactive NLP.

Karen Hambardzumyan

Karen is interested in interpretability and faithfulness of LLMs. His research focuses on understanding and improving how these complex...

Linqing Liu

Linqing is a first year PhD student with broad interests in NLP and Machine Learning. She is currently working on question answering.

Lovish Madaan

Lovish is interested in NLP and large language models, specifically evaluations, generalization, and efficiency.

Max Bartolo

Max’s current research focuses on natural language processing, with particular interest in question answering and machine reasoning.

PhD student

Sohee Yang is a PhD student/research scientist intern at UCL/DeepMind, exploring ways to enhance the reasoning of NLP/ML systems.

Yao is interested in everything.

Yihong Chen

Yihong is interested in almost everything, currently working on methods that empower efficient learning of symbols, particularly...

Yuxiang is a third-year PhD student, interested in Question Answering, Knowledge Base, and other knowledge related tasks.

Alice Winters

Group Administrator

Alice joined the NLP group in September 2022 as PA to Pontus Stenetorp and group administrator.

Tim Rocktäschel

Now a Postdoc at Oxford University

Former Affiliated Faculty (Associate Professor)

Pasquale Minervini

Now an Associate Professor at Edinburgh University

Former Senior Research Fellow, Principal Investigator for H2020 CLARIFY

Luca Franceschi

Now a Research Scientist at Amazon

Former Research Fellow

Maximilian Mozes

Now a Member of Technical Staff at Cohere

Former PhD Student

Patrick Lewis

Now a Research Scientist at FAIR

Tom Crossland

Now a a Teaching Fellow at Imperial College London

Matko Bošnjak

Now a Research Scientist at DeepMind

Alastair Roberts

Alastair’s interests lie in natural language processing & machine learning.

Former Visiting Researcher

Johannes Welbl

Luke hewitt.

Now a PhD student at MIT

Former Intern

Gerasimos Lampouras

Now a research associate at University of Sheffield

Former Research Associate

Saku Sugawara

Now back to being a Ph.D. student at the University of Tokyo.

Former Visiting PhD Student

Sonse Shimaoka

Now a master student at Tohoku University

Now back to being a PhD student at the Chinese Academy of Sciences.

Andreas Vlachos

Now a senior lecturer at University of Cambridge

Guillaume Bouchard

Now CEO at CheckStep

Former Senior Research Associate

Thomas Demeester

Now a post-doc at University of Ghent

Jason Naradowsky

Now a research scientist at Preferred Networks (PFN)

Théo Trouillon

Now back to being a PhD student at Xerox Research Centre Europe

Marzieh Saeidi

Now a Research Scientist at Facebook

Former Research Scientist at Facebook

Isabelle Augenstein

Now an associate professor at University of Copenhagen

Naoya Inoue

Now an assistant professor at Tohoku University

Tim Dettmers

Now a PhD student at University of Washington

V. Ivan Sanchez

Now an NLP researcher at Lenovo

Andres Campero

Now back to being a PhD student at MIT

Takuma Yoneda

Now a student at Toyota Technological Institute at Chicago

Georgios Spithourakis

Now a ML engineer at PolyAI

Publications

Models in the loop: aiding crowdworkers with generative annotation assistants, fantastically ordered prompts and where to find them: overcoming few-shot prompt order sensitivity, contrasting human- and machine-generated word-level adversarial examples for text classification, relation prediction as an auxiliary training objective for improving multi-relational graph representations, implicit mle: backpropagating through discrete exponential family distributions}, improving question answering model robustness with synthetic adversarial data generation.

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A synthetic dataset of 315k QA pairs on passages from SQuAD designed to help make QA models more robust to human adversaries. This resource is also available in HuggingFace datasets at https://huggingface.co/datasets/mbartolo/synQA.

PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them

Adversarialqa (from beat the ai).

A dataset of 36k challenging extractive QA pairs consisting of training, evaluation and test data collected using three different models-in-the-loop: BiDAF, BERT and RoBERTa.

KILT: a Benchmark for Knowledge Intensive Language Tasks

A resource for training, evaluating and analyzing NLP models on Knowledge Intensive Language Tasks. KILT has been built from 11 datasets representing 5 tasks.

A multi-way aligned extractive QA evaluation benchmark MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.

ShARC: Shaping Answers with Rules through Conversation

A collection of 32k task instances based on real-world rules and crowd-generated questions and scenarios requiring both the interpretation of rules and the application of background knowledge.

WikiHop & MedHop (QAngaroo)

Multi-hop question answering datasets from two different domains, designed to enabe models to combine disjoint pieces of textual evidence.

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Linguistics, PhD

The Ph.D. program in Linguistics at Penn embraces a wide range of theoretical specializations and methodologies. What unites them is a commitment to careful and explicit formal analysis of the human capacity for learning and using language.

The core of our program is the formal generative tradition, but we encourage the cross-fertilization that results from the confrontation of empirical and theoretical perspectives on language structure. By our close collaboration with other programs (such as computer science and psychology) we promote an awareness of the broad view of language that interdisciplinary study induces. In addition to broad training, students are offered and expected to master the methods and results of their chosen areas of concentration in linguistics as a prerequisite to fruitful engagement in dialogue with others, both within and outside the program.

For more information: https://www.ling.upenn.edu/graduate/

View the University’s Academic Rules for PhD Programs .

Required Courses

The total course units required for graduation is 20. A minimum of 12 course units must be taken at the University of Pennsylvania.

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2023 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

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PhD in Linguistics

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• Linguistic Semantics Lab
• Phonology Lab
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Aims of the PhD

Human language is a multifaceted phenomenon. It is simultaneously a property of individual minds and of whole speech communities, and thus both internal and external to us. It both shapes and is shaped by our societies over time. It is a combination of sound (or sign), which has physical properties that can be measured, and meaning, which does not. Accordingly, becoming a linguistic researcher involves mastering a variety of methods, both quantitative and qualitative. The PhD in Linguistics at BU aims to produce scholars who are versatile enough to be experts in both of these aspects of linguistic inquiry, yet skilled enough to do cutting-edge research in a particular subfield of the discipline. We offer a solid grounding in a range of research methods, including field methods, quantitative methods, and computational methods.

Learning Outcomes

Students graduating with a PhD in Linguistics will demonstrate:

• broad knowledge of the discipline
• deeper knowledge in a specialized area or subfield
• ability to carry out a significant piece of independent research (which implies knowledge of and ability to use research methodologies in order to complete the research)

Prerequisites

The GRE (Graduate Record Examination) is not required to apply.

Entering students are expected to have completed introductory classes in: 

• phonetics/phonology (e.g., GRS LX 601)
• syntax (e.g., GRS LX 621)
• semantics/pragmatics (e.g., GRS LX 631)

Students who do not have sufficient background in linguistics must complete additional coursework to fulfill the above prerequisites prior to entry or during the first year. Note: if completed at BU, GRS LX 601, 621, and 631 will not count toward the PhD course requirements.

Admissions & Funding

The deadline for application to enter the program in Fall 2023 is January 6, 2023.  Information about the graduate admissions process ( including the application process and requirements ) is available at the Graduate School of Arts & Sciences (GRS) website:

We anticipate being able to admit about five students per year. All admitted students will receive full coverage of tuition costs plus a fellowship for five years. For further information about funding, consult the GRS website above.

Requirements

Course requirements.

The PhD requires successful completion of 64 credits at the graduate level, including three core courses: 

• GRS LX 703 Phonological Analysis
• GRS LX 722 Intermediate Syntax
• GRS LX 732 Intermediate Semantics

Six additional courses from the four areas below, with two courses each in two of the areas, and one course each in the remaining two areas:

• advanced phonetics, phonology, or morphology (e.g., GRS LX 706)
• advanced syntax, semantics, or pragmatics (e.g., GRS LX 723, 736)
• linguistic research methodology
• language acquisition or socio-historical linguistics

A 4-credit graduate proseminar sequence (GRS LX 801 & 802) is typically taken in the second year.

Finally, six additional courses (including up to 8 credits of directed study) are taken in Linguistics or related fields that comprise a specialization , which will generally be in the area of the dissertation. These courses will be decided upon by the student in conjunction with their advisor, whose approval is required.

Language Requirement

The PhD requires demonstration of graduate-level reading proficiency in two foreign languages (one of which may be English, for non-native speakers) by the end of the third year of enrollment.

These proficiencies can be demonstrated through any of:

• a language examination
• successful completion of a non-credit graduate-level foreign language reading course offered at BU
• the equivalent of two years of undergraduate study of the language at BU (or successful completion of any higher-level language course taught in the language)

Graduate-level foreign language reading courses offered at BU include:

• GRS LF 621 Reading French for Graduate Students
• GRS LG 621 Reading German for Graduate Students
• GRS LI 621 Reading Italian for Graduate Students
• GRS LS 621 Reading Spanish for Graduate Students

Qualifying Examinations

To advance to candidacy, students must satisfactorily complete and defend two substantial research papers in different areas of the field (the first by the end of the fourth semester, the second by the end of the sixth semester of enrollment).

Each Qualifying Paper (QP) will be planned and carried out under the supervision of a Linguistics faculty member with expertise appropriate to the relevant project and, upon completion, will be defended orally and approved by an examining committee, composed of the first and second reader as well as a third faculty member determined by the Director of Graduate Studies (DGS) in consultation with the student.

A brief proposal for each QP must be submitted, with signed approval of a first and second reader (who have been approved by the DGS and who have agreed to advise the student on the proposed project), by October 15 of the academic year in which the project is to be completed. For the second QP, a topic approval form, in which the student explains how the second QP differs from their first QP, must also be submitted, in advance of the proposal approval form.

Dissertation and Final Oral Examination

PhD candidates will demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship.

A prospectus for the dissertation must be completed and approved by the readers, the DGS, and the Department Chair.

Candidates must undergo a final oral examination in which they defend their dissertation as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation.

All portions of the dissertation and final oral examination must be completed as outlined in the GRS general requirements for the PhD degree:

Director of Graduate Studies

Co-Directors of Graduate Admissions