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Title: massive multilingual abstract meaning representation: a dataset and baselines for hallucination detection.

Abstract: Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. There has been substantial work developing AMR corpora in English and more recently across languages, though the limited size of existing datasets and the cost of collecting more annotations are prohibitive. With both engineering and scientific questions in mind, we introduce MASSIVE-AMR, a dataset with more than 84,000 text-to-graph annotations, currently the largest and most diverse of its kind: AMR graphs for 1,685 information-seeking utterances mapped to 50+ typologically diverse languages. We describe how we built our resource and its unique features before reporting on experiments using large language models for multilingual AMR and SPARQL parsing as well as applying AMRs for hallucination detection in the context of knowledge base question answering, with results shedding light on persistent issues using LLMs for structured parsing.

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Abstract meaning representation for legal documents: an empirical research on a human-annotated dataset

• Original Research
• Published: 07 July 2021
• Volume 30 , pages 221–243, ( 2022 )

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• Sinh Trong Vu 1 ,
• Minh Le Nguyen 1 &
• Ken Satoh 2  

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Natural language processing techniques contribute more and more in analyzing legal documents recently, which supports the implementation of laws and rules using computers. Previous approaches in representing a legal sentence often based on logical patterns that illustrate the relations between concepts in the sentence, often consist of multiple words. Those representations cause the lack of semantic information at the word level. In our work, we aim to tackle such shortcomings by representing legal texts in the form of abstract meaning representation (AMR), a graph-based semantic representation that gains lots of polarity in NLP community recently. We present our study in AMR Parsing (producing AMR from natural language) and AMR-to-text Generation (producing natural language from AMR) specifically for legal domain. We also introduce JCivilCode, a human-annotated legal AMR dataset which was created and verified by a group of linguistic and legal experts. We conduct an empirical evaluation of various approaches in parsing and generating AMR on our own dataset and show the current challenges. Based on our observation, we propose our domain adaptation method applying in the training phase and decoding phase of a neural AMR-to-text generation model. Our method improves the quality of text generated from AMR graph compared to the baseline model. (This work is extended from our two previous papers: “An Empirical Evaluation of AMR Parsing for Legal Documents”, published in the Twelfth International Workshop on Juris-informatics (JURISIN) 2018; and “Legal Text Generation from Abstract Meaning Representation”, published in the 32nd International Conference on Legal Knowledge and Information Systems (JURIX) 2019.).

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An Empirical Evaluation of AMR Parsing for Legal Documents

Bringing order into the realm of Transformer-based language models for artificial intelligence and law

JNLP Team: Deep Learning Approaches for Tackling Long and Ambiguous Legal Documents in COLIEE 2022

We keep the original trained models without retrained on the new dataset LDC2017T10

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Acknowledgments

This work was supported by JST CREST Grant Number JPMJCR1513 Japan, JSPS Kakenhi Grant Number 20H04295, 20K2046, and 20K20625. The research also was supported in part by the Asian Office of Aerospace R\&D (AOARD), Air Force Office of Scientific Research (Grant no. FA2386-19-1-4041).

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Sinh Trong Vu & Minh Le Nguyen

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Vu, S.T., Le Nguyen, M. & Satoh, K. Abstract meaning representation for legal documents: an empirical research on a human-annotated dataset. Artif Intell Law 30 , 221–243 (2022). https://doi.org/10.1007/s10506-021-09292-6

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DOI : https://doi.org/10.1007/s10506-021-09292-6

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Abstract Meaning Representation parser

Description.

Abstract Meaning Representation (AMR; link ) is a semantic representation language. An AMR is a single-rooted, directed graph representing the meaning of a sentence.

This is a demo for AMREager, an AMR parser that works by processing a given sentence left-to-right, similarly to transition-based dependency parsers.

We thank our funders: Bloomberg and EU H2020 (grant agreement: 688139, SUMMA).

Graph visualization for this demo is done using AMRICA .

AMREager’s source code can be found on github . The multilingual parser can be found on github as well.

We also have developed a set of evaluation metrics for AMR.

The parser is described in detail in the paper that can be downloaded here .

The multilingual parser is described in detail in the following paper that can be found here .

Language: English Chinese German Italian Spanish (non-English parsers are still experimental)

Our parser also gives alignments in a comment below the sentence. Alignments should be interpreted as follows. For example,

means that the second token (x-y refers to the tokens that come between words x and y exclusive of y) aligned with the first child of the root node (0.0) and the second child of the root node (0.1). The address 0.x.y… refers to the path from the root to its xth child, then yth child, until the end of the path.

A full alignment would be an output such as

appearing after the sentence line.

A bstract M eaning R epresentation for Gesture

Richard Brutti , Lucia Donatelli , Kenneth Lai , James Pustejovsky

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[Abstract Meaning Representation for Gesture](https://aclanthology.org/2022.lrec-1.169) (Brutti et al., LREC 2022)

• Abstract Meaning Representation for Gesture (Brutti et al., LREC 2022)
• Richard Brutti, Lucia Donatelli, Kenneth Lai, and James Pustejovsky. 2022. Abstract Meaning Representation for Gesture . In Proceedings of the Thirteenth Language Resources and Evaluation Conference , pages 1576–1583, Marseille, France. European Language Resources Association.

Abstract Meaning Representation (AMR) Annotation Release 2.0

Introduction

Abstract Meaning Representation (AMR) Annotation Release 2.0 was developed by the Linguistic Data Consortium (LDC), SDL/Language Weaver, Inc. , the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It contains a sembank (semantic treebank) of over 39,260 English natural language sentences from broadcast conversations, newswire, weblogs and web discussion forums.

AMR captures “who is doing what to whom” in a sentence. Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure. AMR utilizes PropBank frames, non-core semantic roles, within-sentence coreference, named entity annotation, modality, negation, questions, quantities, and so on to represent the semantic structure of a sentence largely independent of its syntax.

LDC also released Abstract Meaning Representation (AMR) Annotation Release 1.0 ( LDC2014T12 ).

The source data includes discussion forums collected for the DARPA BOLT and DEFT programs, transcripts and English translations of Mandarin Chinese broadcast news programming from China Central TV, Wall Street Journal text, translated Xinhua news texts, various newswire data from NIST OpenMT evaluations and weblog data used in the DARPA GALE program. The following table summarizes the number of training, dev, and test AMRs for each dataset in the release. Totals are also provided by partition and dataset:

For those interested in utilizing a standard/community partition for AMR research (for instance in development of semantic parsers), data in the "split" directory contains 39,260 AMRs split roughly 93%/3.5%/3.5% into training/dev/test partitions, with most smaller datasets assigned to one of the splits as a whole. Note that splits observe document boundaries. The "unsplit" directory contains the same 39,260 AMRs with no train/dev/test partition.

Please view this sample .

None at this time.

Acknowledgements

From University of Colorado

We gratefully acknowledge the support of the National Science Foundation Grant NSF: 0910992 IIS:RI: Large: Collaborative Research: Richer Representations for Machine Translation and the support of Darpa BOLT - HR0011-11-C-0145 and DEFT - FA-8750-13-2-0045 via a subcontract from LDC. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, DARPA or the US government.

From Information Sciences Institute (ISI)

Thanks to NSF (IIS-0908532) for funding the initial design of AMR, and to DARPA MRP (FA-8750-09-C-0179) for supporting a group to construct consensus annotations and the AMR Editor. The initial AMR bank was built under DARPA DEFT FA-8750-13-2-0045 (PI: Stephanie Strassel; co-PIs: Kevin Knight, Daniel Marcu, and Martha Palmer) and DARPA BOLT HR0011-12-C-0014 (PI: Kevin Knight).

From Linguistic Data Consortium (LDC)

This material is based on research sponsored by Air Force Research Laboratory and Defense Advance Research Projects Agency under agreement number FA8750-13-2-0045. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory and Defense Advanced Research Projects Agency or the U.S. Government.

We gratefully acknowledge the support of Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0184 Subcontract 4400165821. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the DARPA, AFRL, or the US government.

From Language Weaver (SDL)

This work was partially sponsored by DARPA contract HR0011-11-C-0150 to LanguageWeaver Inc. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the DARPA or the US government.

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During discourse comprehension, every new word adds to an evolving representation of meaning that accumulates over consecutive sentences and constrains the next words. To minimize repetition and utterance length, languages use pronouns, like the word ‘she’, to refer to nouns and phrases that were previously introduced. It has been suggested that language comprehension requires that pronouns activate the same neuronal representations as the nouns themselves. Here, we test this hypothesis by recording from individual neurons in the human hippocampus during a reading task. We found that cells that are selective to a particular noun are later reactivated by pronouns that refer to the cells’ preferred noun. These results imply that concept cells contribute to a rapid and dynamic semantic memory network which is recruited during language comprehension. This study uniquely demonstrates, at the single-cell level, how memory and language are linked.

Competing Interest Statement

The authors have declared no competing interest.

One-Sentence Summary: Pronouns activate neurons in the human hippocampus if they refer to the concepts to which the cells are tuned.

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Prescribed mean curvature problems on homogeneous vector bundles

• Correa, Eder M.

In this paper, we provide a detailed and systematic study of weak (singular) Hermite-Einstein structures on homogeneous holomorphic vector bundles over rational homogeneous varieties. We use standard tools from spectral geometry, Harmonic analysis, and Cartan's highest weight theory to provide a sufficient condition in terms of Fourier series and intersection numbers under which an $L^{2}$-function can be realized as mean curvature of a singular Hermitian structure on an irreducible homogeneous holomorphic vector bundle. We prove that the condition provided is necessary and sufficient for functions that belong to certain interpolation spaces. In the particular case of line bundles over irreducible Hermitian symmetric spaces of compact type, we describe explicitly in terms of representation theory the solutions of the underlying geometric PDE. Also, we establish a sufficient condition in terms of Fourier coefficients and intersection numbers for solvability and convergence of the weak Hermite-Einstein flow on irreducible homogeneous holomorphic vector bundles. As an application of our methods, we describe the first explicit examples in the literature of solutions to several geometric flows, including Donaldson's heat flow, Yang-Mills heat flow, and the gradient flow of Donaldson's Lagrangian, on line bundles over irreducible Hermitian symmetric spaces of compact type. Additionally, we show that every polynomial central charge function gives rise to a weak Donaldson's Lagrangian $\mathcal{M}$. In the particular case of irreducible homogeneous holomorphic vector bundles, we prove that the gradient flow of $\mathcal{M}$ converges to a Hermitian structure which is, up to a gauge transformation, a $Z$-critical Hermitian structure in the large volume limit.

• Mathematics - Differential Geometry;
• Mathematics - Algebraic Geometry;
• Mathematics - Analysis of PDEs;
• Mathematics - Representation Theory;
• Mathematics - Spectral Theory