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REVIEW 3 major objections 4 minor 126 references

Survey of Abstract Meaning Representation: Then, Now, Future

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This survey maps Abstract Meaning Representation from its 2013 origins to current state-of-the-art parsing and generation systems, arguing the graph-based meaning format is now a mature, multilingual, application-spanning framework.

desk verdict Competent, useful AMR survey that needs a solid copyedit of its references and SOTA claims before it can be trusted as a literature map. read the letter →

arxiv 2505.03229 v1 pith:WQ4STT3F submitted 2025-05-06 cs.CL

Behrooz Mansouri This is my paper · ORCID
classification cs.CL
keywords AbstractMeaningRepresentationAMRsurveysemanticparsingAMR-to-textgenerationmultilingualSMatchdownstreamNLPapplicationsgraph-basedsemantics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper surveys Abstract Meaning Representation (AMR), a graph-based meaning format where nodes are concepts and edges are semantic roles, and organizes the field into then, now, and future. It aims to be a comprehensive entry point: it explains what AMR captures and omits, reviews parsing (text to AMR) and generation (AMR to text), covers multilingual adaptation and downstream applications, and identifies current state-of-the-art systems. A sympathetic reader would take away that AMR has grown from a single English annotation project into a multilingual, application-spanning framework whose next phase is likely tied to large language models and extended representations such as UMR.

What carries the argument

The load-bearing object is the AMR graph itself: a rooted, directed, acyclic graph whose nodes are concepts, often PropBank framesets, and whose edges are semantic relations such as :ARG0, :ARG1, :polarity, and :mode. It is written in PENMAN notation, with variables allowing re-entrant nodes for coreference. This single representation carries the survey's entire argument because every task, parsing, generation, cross-lingual adaptation, and downstream application, is defined as an operation on the same graph structure, and every evaluation metric measures overlap between graph triples.

What would settle it

Run a publicly available AMR parser released after 2024 on the AMR 2.0 and AMR 3.0 test sets and compare SMatch scores; if it exceeds 86.1 on AMR 2.0 or 84.6 on AMR 3.0, the survey's state-of-the-art parsing claim is false. Similarly, if a generator released after DualGen exceeds BLEU 51.6 or 51.8 on the same benchmarks, the state-of-the-art generation claim is false.

Watch

Extended reading notes

Core claim

The paper's central discovery is that AMR has matured from a sentence-level English semantic annotation scheme, introduced in 2013, into a stable graph representation with an active ecosystem: three LDC corpora, mature evaluation metrics led by SMatch, neural parsers and generators, multilingual corpora including a 52-language dataset, and documented use in summarization, translation, classification, extraction, and question answering. It identifies LeakDistill as the current state-of-the-art parser, with SMatch scores of 86.1 and 84.6 on AMR 2.0 and 3.0, and DualGen as the state-of-the-art generator, with BLEU scores of 51.6 and 51.8. The survey also argues that AMR's known limitations, such as tense, aspect, word order, ambiguity, figurative language, and sentiment, are being addressed through enrichment schemes, document-level extensions, and proposals like Uniform Meaning Representation.

Load-bearing premise

The survey assumes its description of the field is current: if newer systems have overtaken LeakDistill and DualGen, or if the cited papers do not accurately represent the state of the art, the 'now' of the survey is wrong.

Editorial extensions

If this is right

  • Researchers can treat LeakDistill and DualGen as reasonable baseline choices for AMR parsing and generation on the standard LDC corpora, since the survey positions them as current best systems.
  • AMR can serve as an intermediate meaning representation in text-to-text applications: the survey documents pipelines that parse to AMR, merge or edit the graph, and generate new text for summarization, paraphrase, data augmentation, and machine translation.
  • Multilingual AMR is practical without large manual annotation efforts, because silver data via translation and cross-lingual distillation has produced usable parsers and corpora such as MASSIVE-AMR.
  • Few-shot prompting of large language models yields valid AMR graphs, so prompt-based parsing and generation are plausible next-generation approaches rather than speculative ones.
  • Extensions such as enriched AMR, DOCAMR, Dialogue-AMR, Gesture AMR, and MathAMR indicate that the AMR format can be adapted to cover phenomena and modalities the original scheme omitted.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The survey's own timeline implies a shift the author does not fully spell out: if LLMs can parse and generate AMR in few-shot settings, curated sentence-level AMR corpora may become less central to making AMR useful.
  • A testable extension of the survey's map is to use its resource table as a reproducibility checklist; a user who follows the listed tools and scores should be able to reproduce the reported state of the art, so any entry-level discrepancies would reveal the map's limits.
  • The survey's emphasis on English-rooted AMR suggests that cross-lingual utility claims should be read with caution, since source-language influence on AMR structure is documented and may affect fair comparison across languages.
  • The growing list of multimodal AMR variants, for image, gesture, and math, suggests that AMR's next practical role may be as a common graph language connecting text, vision, and structured knowledge, though the survey itself does not demonstrate this.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This manuscript surveys Abstract Meaning Representation (AMR), covering the graph formalism, annotation releases and enrichment methods, text-to-AMR parsing, AMR-to-text generation, multilingual adaptation, and downstream applications. Each task is organized into 'Then, Now, Future' subsections, with illustrative figures for parser and generator architectures and a final table of resources and tools. The survey identifies LeakDistill as the current state-of-the-art parser and DualGen as the current state-of-the-art generator, and it closes with discussion of open challenges and future directions.

Significance. If the bibliographic and state-of-the-art identifications are corrected, this survey would serve as a useful entry point and literature map for AMR research. Its strengths are breadth, covering over 100 references including recent work; clear pedagogical figures for parsing and generation architectures; and a resource table with repository links. The survey performs no derivations, so circularity is not a technical concern, and the author's self-citations in the applications section are descriptive rather than self-confirming. However, because the central value of the manuscript is its reliability as a literature map, the citation errors described below are consequential and should be fixed before publication.

major comments (3)
  1. [Section 2.2 and references [79] and [80]] The two core corpus references are bibliographically indistinguishable: reference [80], introduced as AMR 2.0, has the same title ('Abstract Meaning Representation (AMR) Annotation Release 1.0'), the same year, and the same DOI (10.35111/0YNC-7404) as reference [79], so a reader using the reference list alone is directed to AMR 1.0. In the same passage, the LDC catalog identifiers are incorrect: LDC2014T123 should be LDC2014T12, LDC2017T104 should be LDC2017T10, and LDC2020T025 should be LDC2020T02, even though the correct URLs appear in footnotes 3-5 and in Table 3. These are internally checkable errors in the survey's literature-map function and must be corrected.
  2. [Sections 3.4 and 4.3] The state-of-the-art identifications are not anchored to a date, test split, or comparison set. Section 3.4 states that LeakDistill 'achieves Smatch scores of 86.1 and 84.6 on AMR 2.0 and AMR 3.0, respectively, positioning it as a current state-of-the-art AMR parser' based on a 2023 paper, and Section 4.3 gives DualGen BLEU scores of 51.6 and 51.8 with no venue, year, or data split. Since the manuscript is dated May 2025, the 'Now' framing requires a statement such as 'as of [date], to our knowledge' together with a comparison against published leaderboard entries from intervening work; otherwise readers cannot tell whether the reported numbers still represent the frontier.
  3. [Table 3] The resource table contains a citation mismatch: the row 'XS2match [29]' points to reference [29], which is Feng et al., 'Language-agnostic BERT Sentence Embedding', not an AMR evaluation metric. In addition, reference [40] for DualGen is incomplete, as it lists no publication venue and no arXiv identifier, and the table misspells 'LeakDistill' as 'LeaKDistill' and 'SemBleu' as 'SemBlue'. Because the table is a central part of the survey's resource-map function, these entries need verification and correction.
minor comments (4)
  1. [Figure 1 and Section 2] The manuscript uses 'PennMan notation'; the correct name of the notation is PENMAN.
  2. [Section 3.4] The phrase 'cross-sentential coreferene' contains a typo and should read 'cross-sentential coreference'.
  3. [Section 3.3] The text 'view ARM parsing as a two-stage process' appears to contain a typo and should read 'view AMR parsing as a two-stage process'.
  4. [Section 3.1 and Table 3] The metric is spelled both 'SemBleu' and 'SemBlue'; the cited work uses the spelling 'SemBleu', so the spelling should be unified.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey is descriptive and its self-citations are not load-bearing.

full rationale

The manuscript is a survey, not a derivation or prediction paper. Its central claim is to organize and describe AMR research, and this claim rests on the cited literature and on the accuracy of its resource table, not on any fitted parameter, definitional equivalence, or self-referential uniqueness theorem. The author's own works (MathAMR [65] and timeline generation [64]) are cited as examples of AMR enrichment and applications, but these citations are descriptive and do not function as evidence for any derived result. The internally checkable problem that refs [79] and [80] share the same title, year, and DOI even though Section 2.2 identifies AMR 2.0 as LDC2017T10 is a bibliographic correctness issue, not a circularity issue. Similarly, the state-of-the-art identifications in Sections 3.4 and 4.3 may require a dated leaderboard anchor, but that is a verification concern rather than a circular reduction. No equation, fitted input, or self-citation chain is used to produce a conclusion, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

No numerical parameters or novel entities are introduced. The only load-bearing premise is the reliability and representativeness of the cited literature, which is standard for a survey.

assumptions (1)
  • domain assumption The cited works accurately report their own results and are representative of the AMR literature.
    The survey is a synthesis; it does not re-derive or re-run the cited experiments, so its descriptive claims inherit the correctness of the sources. This is a standard assumption for survey writing.

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Cite this review

Pith. "Pith review of Survey of Abstract Meaning Representation: Then, Now, Future." pith.science (2026). https://pith.science/paper/WQ4STT3F

@misc{pith2026250503229,
  author       = {Pith},
  title        = {Pith review of: Survey of Abstract Meaning Representation: Then, Now, Future},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WQ4STT3F}},
  note         = {Machine review of arXiv:2505.03229}
}
read the original abstract

This paper presents a survey of Abstract Meaning Representation (AMR), a semantic representation framework that captures the meaning of sentences through a graph-based structure. AMR represents sentences as rooted, directed acyclic graphs, where nodes correspond to concepts and edges denote relationships, effectively encoding the meaning of complex sentences. This survey investigates AMR and its extensions, focusing on AMR capabilities. It then explores the parsing (text-to-AMR) and generation (AMR-to-text) tasks by showing traditional, current, and possible futures approaches. It also reviews various applications of AMR including text generation, text classification, and information extraction and information seeking. By analyzing recent developments and challenges in the field, this survey provides insights into future directions for research and the potential impact of AMR on enhancing machine understanding of human language.

Figures

Figures reproduced from arXiv: 2505.03229 by the authors.

Figure 1
Figure 1. Abstract Meaning Representation for five different input sentences (A) with the same semantics. (B) shows the AMR with [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. MathAMR [65] for input sentence “Find 𝑥 𝑛 + 𝑦 𝑛 + 𝑧 𝑛 general solution”. (A) AMR is generated for the input text, with the formula replaced with a placeholder (PL) for the formula. Then the operator tree representation of the formula (B), is integrated into the AMR, replacing the placeholder in AMR with the root of the operator tree, resulting in MathAMR shown in (C). parsing has been explored, and future research i… view at source ↗
Figure 3
Figure 3. Transition-based and Alignment-based approaches for AMR Parsing. Transition-based methods incrementally generate AMRs [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Overview of current AMR parsing approaches. (A) Seq-to-seq models generate a linearized AMR, and (B) Graph prediction [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: JAMR [31] approach for text generation from Abstract Meaning Representation. (a) The input AMR graph is processed to remove re-entrancies, yielding a tree structure. (b) This tree is then used by a tree-to-text transducer, which (c) generates the final text output. tra…
Figure 6
Figure 6. Figure 6: Neural AMR-to-text generation approaches. The top section illustrates graph-to-seq models, where a graph encoder directly [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: AMR Parsing and AMR-to-text Generation Tasks for Non-English Languages. Approaches shown are: (A) direct AMR parsing [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: Application of AMR for summarization (Adapted from Liu et al [ [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Application of AMR for text classification. The input text is parsed to generate the AMR. Features from AMR can be derived [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: Application of AMR for Question-Answering. The AMR on the left corresponds to the question ‘Who directed The Godfather [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]

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Works this paper leans on

126 extracted references · 29 canonical work pages

  1. [80]

    Palmer, Martha, Marcu, Daniel, Griffitt, Kira, Knight, Kevin, Baranescu, Laura, Bonial, Claire, Georgescu, Madalina, Hermjakob, Ulf, and Schneider, Nathan. 2014. Abstract Meaning Representation (AMR) Annotation Release 1.0. https://doi.org/10.35111/0YNC-7404

    work page doi:10.35111/0ync-7404 2014

  2. [29]

    Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2022. Language-agnostic BERT Sentence Embedding. InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational Linguistics, Dublin, ...

    doi:10.18653/v1/2022.acl-long.62 2022

  3. [40]

    Yining Hong, Fanchao Qi, and Maosong Sun. 2024. Two Heads Are Better Than One: Exploiting Both Sequence and Graph Models in AMR-To-Text Generation. (2024)

    2024

  4. [1]

    Mohamed Ashraf Abdelsalam, Zhan Shi, Federico Fancellu, Kalliopi Basioti, Dhaivat Bhatt, Vladimir Pavlovic, and Afsaneh Fazly. 2022. Visual Semantic Parsing: From Images to Abstract Meaning Representation. In Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL), Antske Fokkens and Vivek Srikumar (Eds.). Association for Com...

    work page doi:10.18653/v1/2022.conll-1.19 2022

  5. [2]

    Omri Abend and Ari Rappoport. 2017. The State of the Art in Semantic Representation. InProceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Regina Barzilay and Min-Yen Kan (Eds.). Association for Computational Linguistics, Vancouver, Canada, 77–89. https://doi.org/10.18653/v1/P17-1008

    work page doi:10.18653/v1/p17-1008 2017

  6. [3]

    Anchieta, Marco A

    Rafael T. Anchieta, Marco A. S. Cabezudo, and Thiago A. S. Pardo. 2019. SEMA: an Extended Semantic Evaluation Metric for AMR. arXiv:1905.12069 [cs.CL] https://arxiv.org/abs/1905.12069

    arXiv 2019

  7. [4]

    Zahra Azin and Gülşen Eryiğit. 2019. Towards Turkish Abstract Meaning Representation. InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop , Fernando Alva-Manchego, Eunsol Choi, and Daniel Khashabi (Eds.). Association for Computational Linguistics, Florence, Italy, 43–47. https://doi.org/10.1...

    work page doi:10.18653/v1/p19-2006 2019

  8. [5]

    Xuefeng Bai, Yulong Chen, Linfeng Song, and Yue Zhang. 2021. Semantic Representation for Dialogue Modeling. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (E...

    work page doi:10.18653/v1/2021.acl-long.342 2021

Show all 126 references
  1. [6]

    Xuefeng Bai, Yulong Chen, and Yue Zhang. 2022. Graph Pre-training for AMR Parsing and Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds....

    2022 doi

  2. [7]

    Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013. Abstract Meaning Representation for Sembanking. In Proceedings of the 7th Linguistic Annotation Workshop and Inter...

    2013

  3. [8]

    Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization , Jade Goldstei...

    2005

  4. [9]

    Michele Bevilacqua, Rexhina Blloshmi, and Roberto Navigli. 2021. One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 12564–12573

    2021

  5. [10]

    Michele Bevilacqua, Rexhina Blloshmi, and Roberto Navigli. 2021. One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline. Proceedings of the AAAI Conference on Artificial Intelligence 35, 14 (May 2021), 12564–12573. https: //doi.o...

    2021 doi

  6. [11]

    Rexhina Blloshmi, Rocco Tripodi, and Roberto Navigli. 2020. XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Bonnie Webber, Trevor Cohn, Yulan He, a...

    2020 doi

  7. [12]

    Austin Blodgett and Nathan Schneider. 2021. Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Con...

    2021 doi

  8. [13]

    Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, and Clare Voss

    Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, and Clare Voss

  9. [14]

    Lukin, David Doughty, Steven Hill, and Clare Voss

    Claire Bonial, Stephanie M. Lukin, David Doughty, Steven Hill, and Clare Voss. 2020. InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research. In Proceedings of the Second International Workshop on Designing Meaning Representations , Nianwen Xue, Johan Bos, Willi...

    2020

  10. [15]

    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 , Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christ...

    2022

  11. [16]

    Deng Cai, Xin Li, Jackie Chun-Sing Ho, Lidong Bing, and Wai Lam. 2021. Multilingual AMR Parsing with Noisy Knowledge Distillation. InFindings of the Association for Computational Linguistics: EMNLP 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yi...

    2021 doi

  12. [17]

    Shu Cai and Kevin Knight. 2013. Smatch: an Evaluation Metric for Semantic Feature Structures. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , Hinrich Schuetze, Pascale Fung, and Massimo Poesio (Eds.). Associ...

    2013

  13. [18]

    Ziming Cheng, Zuchao Li, and Hai Zhao. 2022. BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning. In Proceedings of the 29th International Conference on Computational Linguistics, Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner,...

    2022

  14. [19]

    Hyonsu Choe, Jiyoon Han, Hyejin Park, Tae Hwan Oh, and Hansaem Kim. 2020. Building Korean Abstract Meaning Representation Corpus. In Proceedings of the Second International Workshop on Designing Meaning Representations , Nianwen Xue, Johan Bos, William Croft, Jan Hajič, Chu-Re...

    2020

  15. [20]

    Marco Damonte and Shay Cohen. 2022. Abstract Meaning Representation 2.0 - Four Translations. https://doi.org/11272.1/AB2/5OU0AQ

    2022

  16. [21]

    Marco Damonte and Shay B. Cohen. 2018. Cross-Lingual Abstract Meaning Representation Parsing. InProceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) , Marilyn Walk...

    2018 doi

  17. [22]

    Marco Damonte and Shay B. Cohen. 2019. Structural Neural Encoders for AMR-to-text Generation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , J...

    2019 doi

  18. [23]

    Cohen, and Giorgio Satta

    Marco Damonte, Shay B. Cohen, and Giorgio Satta. 2017. An Incremental Parser for Abstract Meaning Representation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers , Mirella Lapata, Phil Blunso...

    2017

  19. [24]

    Zhenyun Deng, Yonghua Zhu, Yang Chen, Michael Witbrock, and Patricia Riddle. 2022. Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 , Lud De R...

    2022 doi

  20. [25]

    Lucia Donatelli, Michael Regan, William Croft, and Nathan Schneider. 2018. Annotation of Tense and Aspect Semantics for Sentential AMR. In Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LA W-MWE-CxG-2018) , Agata Savary, Ca...

    2018

  21. [26]

    Ermal Elbasani and Jeong-Dong Kim. 2022. AMR-CNN: Abstract Meaning Representation with Convolution Neural Network for Toxic Content Detection. Journal of Web Engineering 21, 3 (2022), 677–692. https://doi.org/10.13052/jwe1540-9589.2135

    2022

  22. [27]

    You Are An Expert Linguistic Annotator

    Allyson Ettinger, Jena Hwang, Valentina Pyatkin, Chandra Bhagavatula, and Yejin Choi. 2023. “You Are An Expert Linguistic Annotator”: Limits of LLMs as Analyzers of Abstract Meaning Representation. In Findings of the Association for Computational Linguistics: EMNLP 2023 , Houd...

    2023 doi

  23. [28]

    Angela Fan and Claire Gardent. 2020. Multilingual AMR-to-Text Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, On...

    2020 doi

  24. [30]

    Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, and Prem Natarajan. 2023. MASSIVE: A 1M-Example Multili...

    2023

  25. [31]

    Smith, and Jaime Carbonell

    Jeffrey Flanigan, Chris Dyer, Noah A. Smith, and Jaime Carbonell. 2016. Generation from Abstract Meaning Representation using Tree Transducers. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language ...

    2016 doi

  26. [32]

    Jeffrey Flanigan, Sam Thomson, Jaime Carbonell, Chris Dyer, and Noah A. Smith. 2014. A Discriminative Graph-Based Parser for the Abstract Meaning Representation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ...

    2014 doi

  27. [33]

    Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar, Chandra Kiran Evuru, Ramaneswaran S, S Sakshi, and Dinesh Manocha. 2024. ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions. In Proceedings of the 62nd Annual Meeting of the Association for Computational L...

    2024 doi

  28. [34]

    Daniel Gildea and Daniel Jurafsky. 2000. Automatic Labeling of Semantic Roles. In Proceedings of the 38th Annual Meeting on Association for Computational Linguistics (Hong Kong)(ACL ’00). Association for Computational Linguistics, USA, 512–520. https://doi.org/10.3115/1075218.1075283

    2000

  29. [35]

    Jonas Groschwitz, Shay Cohen, Lucia Donatelli, and Meaghan Fowlie. 2023. AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , Houda Bouamor, Juan Pino, and...

    2023 doi

  30. [36]

    Shubham Gupta, Narendra Yadav, Suman Kundu, and Sainathreddy Sankepally. 2024. FakEDAMR: Fake News Detection Using Abstract Meaning Representation Network. In Complex Networks & Their Applications XII , Hocine Cherifi, Luis M. Rocha, Chantal Cherifi, and Murat Donduran (Eds.)....

    2024

  31. [37]

    Hardy Hardy and Andreas Vlachos. 2018. Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representa- tion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , Ellen Riloff, David Chiang, Julia Hockenm...

    2018 doi

  32. [38]

    Johannes Heinecke. 2023. metAMoRphosED, a graphical editor for Abstract Meaning Representation. In Proceedings of the 19th Joint ACL- ISO Workshop on Interoperable Semantics (ISA-19) , Harry Bunt (Ed.). Association for Computational Linguistics, Nancy, France, 27–32. https: //...

    2023

  33. [39]

    Johannes Heinecke and Anastasia Shimorina. 2022. Multilingual Abstract Meaning Representation for Celtic Languages. In Proceedings of the 4th Celtic Language Technology Workshop within LREC2022 , Theodorus Fransen, William Lamb, and Delyth Prys (Eds.). European Language Resour...

    2022

  34. [41]

    Alexander Miserlis Hoyle, Ana Marasović, and Noah A. Smith. 2021. Promoting Graph Awareness in Linearized Graph-to-Text Generation. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). 2...

    2021 doi

  35. [42]

    Yilun Hua, Zhaoyuan Deng, and Zhijie Xu. 2022. AMRTVSumm: AMR-augmented Hierarchical Network for TV Transcript Summarization. In Proceedings of The Workshop on Automatic Summarization for Creative Writing , Kathleen Mckeown (Ed.). Association for Computational Linguistics, Gye...

    2022

  36. [43]

    Kuan-Hao Huang, Varun Iyer, I-Hung Hsu, Anoop Kumar, Kai-Wei Chang, and Aram Galstyan. 2023. ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Vol...

    2023 doi

  37. [44]

    Cohen, Xiaohui Yan, and Yi Chang

    Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, and Yi Chang. 2018. Abstract Meaning Representation for Paraphrase Detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies...

    2018 doi

  38. [45]

    Chathuri Jayaweera, Sangpil Youm, and Bonnie J Dorr. 2024. AMREx: AMR for Explainable Fact Verification. InProceedings of the Seventh Fact Extraction and VERification Workshop (FEVER) , Michael Schlichtkrull, Yulong Chen, Chenxi Whitehouse, Zhenyun Deng, Mubashara Akhtar, Rami...

    2024

  39. [46]

    Yuxin Ji, Gregor Williamson, and Jinho D. Choi. 2022. Automatic Enrichment of Abstract Meaning Representations. In Proceedings of the 16th Linguistic Annotation Workshop (LA W-XVI) within LREC2022, Sameer Pradhan and Sandra Kuebler (Eds.). European Language Resources Associati...

    2022

  40. [47]

    Justin Johnson, Andrej Karpathy, and Li Fei-Fei. 2016. DenseCap: Fully Convolutional Localization Networks for Dense Captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    2016

  41. [48]

    Jeongwoo Kang, Maximin Coavoux, Cédric Lopez, and Didier Schwab. 2024. Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2024 , Yaser Al-Onaizan...

    2024 doi

  42. [49]

    Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-S...

    2021

  43. [50]

    Jin-Dong Kim, Yue Wang, Toshihisa Takagi, and Akinori Yonezawa. 2011. Overview of Genia Event Task in BioNLP Shared Task 2011. InProceedings of BioNLP Shared Task 2011 Workshop, Jun’ichi Tsujii, Jin-Dong Kim, and Sampo Pyysalo (Eds.). Association for Computational Linguistics,...

    2011

  44. [51]

    Knight, Kevin, Badarau, Bianca, Baranescu, Laura, Bonial, Claire, Griffitt, Kira, Hermjakob, Ulf, Marcu, Daniel, O’Gorman, Tim, Palmer, Martha, Schneider, Nathan, and Bardocz, Madalina. 2020. Abstract Meaning Representation (AMR) Annotation Release 3.0. https://doi.org/10.3511...

    2020 doi

  45. [52]

    Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017. Neural AMR: Sequence-to-Sequence Models for Parsing and Generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Regi...

    2017 doi

  46. [53]

    Irene Langkilde and Kevin Knight. 1998. Generation that Exploits Corpus-Based Statistical Knowledge. In COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics . https://aclanthology.org/C98-1112

    1998

  47. [54]

    Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. CoRR abs/1910.13461 (2019...

    2019 arXiv

  48. [55]

    Bin Li, Yuan Wen, Weiguang Qu, Lijun Bu, and Nianwen Xue. 2016. Annotating the Little Prince with Chinese AMRs. In Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LA W-X 2016), Annemarie Friedrich and Katrin Tomanek (Eds.). Association...

    2016 doi

  49. [56]

    Changmao Li and Jeffrey Flanigan. 2022. Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers. In Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022) , Li...

    2022 doi

  50. [57]

    Xiang Li, Thien Huu Nguyen, Kai Cao, and Ralph Grishman. 2015. Improving Event Detection with Abstract Meaning Representation. InProceedings of the First Workshop on Computing News Storylines, Tommaso Caselli, Marieke van Erp, Anne-Lyse Minard, Mark Finlayson, Ben Miller, Jord...

    2015 doi

  51. [58]

    Kexin Liao, Logan Lebanoff, and Fei Liu. 2018. Abstract Meaning Representation for Multi-Document Summarization. In Proceedings of the 27th International Conference on Computational Linguistics , Emily M. Bender, Leon Derczynski, and Pierre Isabelle (Eds.). Association for Com...

    2018

  52. [59]

    Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and Noah A. Smith. 2015. Toward Abstractive Summarization Using Semantic Represen- tations. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Languag...

    2015 doi

  53. [60]

    Chunchuan Lyu and Ivan Titov. 2018. AMR Parsing as Graph Prediction with Latent Alignment. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Iryna Gurevych and Yusuke Miyao (Eds.). Association for Computational...

    2018 doi

  54. [61]

    Yu, and Lijie Wen

    Fukun Ma, Xuming Hu, Aiwei Liu, Yawen Yang, Shuang Li, Philip S. Yu, and Lijie Wen. 2023. AMR-based Network for Aspect-based Sentiment Analysis. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Anna Rogers, Jo...

    2023 doi

  55. [62]

    Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, and Salim Roukos. 2020. GPT-too: A Language-Model-First Approach for AMR-to-Text Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Li...

    2020 doi

  56. [63]

    Mann and Eduard H

    William C. Mann and Eduard H. Hovy. 1989. The Penman Language Generation Project. InSpeech and Natural Language: Proceedings of a Workshop Held at Philadelphia, Pennsylvania, February 21-23, 1989 . https://aclanthology.org/H89-1021

    1989

  57. [64]

    Behrooz Mansouri, Ricardo Campos, and Adam Jatowt. 2023. Towards Timeline Generation with Abstract Meaning Representation. In Companion Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW ’23 Companion) . Association for Computing Machinery, New York, NY, USA, 12...

    2023

  58. [65]

    Oard, and Richard Zanibbi

    Behrooz Mansouri, Douglas W. Oard, and Richard Zanibbi. 2022. Contextualized Formula Search Using Math Abstract Meaning Representation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta, GA, USA) (CIKM ’22). Association for ...

    2022

  59. [66]

    Noelia Migueles-Abraira, Rodrigo Agerri, and Arantza Diaz de Ilarraza. 2018. Annotating Abstract Meaning Representations for Spanish. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) , Nicoletta Calzolari, Khalid Choukri,...

    2018

  60. [67]

    Ritwik Mishra and Tirthankar Gayen. 2018. Automatic Lossless-Summarization of News Articles with Abstract Meaning Representation. Procedia Computer Science 135 (2018), 178–185. https://doi.org/10.1016/j.procs.2018.08.164 The 3rd International Conference on Computer Science and...

    2018 doi

  61. [68]

    Rojas Barahona

    Sebastien Montella, Alexis Nasr, Johannes Heinecke, Frederic Bechet, and Lina M. Rojas Barahona. 2023. Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters. In Proceedings of the 17th Conference of the European Chapter o...

    2023 doi

  62. [69]

    Almuth Müller and Achim Kuwertz. 2022. Evaluation of a Semantic Search Approach based on AMR for Information Retrieval in Image Exploitation. In 2022 Sensor Data Fusion: Trends, Solutions, Applications (SDF) . 1–6. https://doi.org/10.1109/SDF55338.2022.9931702

    2022

  63. [70]

    Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, and Nathan Schneider. 2022. DocAMR: Multi-Sentence AMR Representation and Evaluation. In Proceedings of the 2022 Conference of the Nor...

    2022

  64. [71]

    Antonio M. S. Almeida Neto, Helena M. Caseli, and Tiago A. Almeida. 2020. Dense Captioning Using Abstract Meaning Representation. In Intelligent Systems, Ricardo Cerri and Ronaldo C. Prati (Eds.). Springer International Publishing, Cham, 450–465

    2020

  65. [72]

    Long H. B. Nguyen, Viet H. Pham, and Dien Dinh. 2021. Improving Neural Machine Translation with AMR Semantic Graphs. Mathematical Problems in Engineering 2021, 1 (2021), 9939389. https://doi.org/10.1155/2021/9939389

    2021 doi

  66. [73]

    Juri Opitz. 2023. SMATCH++: Standardized and Extended Evaluation of Semantic Graphs. In Findings of the Association for Computational Linguistics: EACL 2023, Andreas Vlachos and Isabelle Augenstein (Eds.). Association for Computational Linguistics, Dubrovnik, Croatia, 1595–160...

    2023 doi

  67. [74]

    Juri Opitz, Angel Daza, and Anette Frank. 2021. Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity. Transactions of the Association for Computational Linguistics 9 (2021), 1425–1441. https://doi.org/10.1162/tacl_a_00435

    2021 doi

  68. [75]

    Juri Opitz, Letitia Parcalabescu, and Anette Frank. 2020. AMR Similarity Metrics from Principles. Transactions of the Association for Computational Linguistics 8 (2020), 522–538. https://doi.org/10.1162/tacl_a_00329 30 Behrooz Mansouri

    2020 doi

  69. [76]

    Juri Opitz, Shira Wein, Julius Steen, Anette Frank, and Nathan Schneider. 2023. AMR4NLI: Interpretable and robust NLI measures from semantic graphs. In Proceedings of the 15th International Conference on Computational Semantics , Maxime Amblard and Ellen Breitholtz (Eds.). Ass...

    2023

  70. [77]

    Christoph Otto, Jonas Groschwitz, Alexander Koller, Xiulin Yang, and Lucia Donatelli. 2024. A Corpus of German Abstract Meaning Representation (DeAMR). In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LR...

    2024

  71. [78]

    Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The Proposition Bank: An Annotated Corpus of Semantic Roles. Comput. Linguist. 31, 1 (March 2005), 71–106. https://doi.org/10.1162/0891201053630264

    2005 doi

  72. [81]

    Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics , Pierre Isabelle, Eugene Charniak, and Dekang Lin (Eds...

    2002

  73. [82]

    Hyeonchu Park, Byungjun Kim, and Bugeun Kim. 2025. DART: An AIGT Detector using AMR of Rephrased Text. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Pa...

    2025

  74. [83]

    Siyana Pavlova, Maxime Amblard, and Bruno Guillaume. 2023. Structural and Global Features for Comparing Semantic Representation Formalisms. In Proceedings of the Fourth International Workshop on Designing Meaning Representations , Julia Bonn and Nianwen Xue (Eds.). Association...

    2023

  75. [84]

    Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. 2017. Addressing the Data Sparsity Issue in Neural AMR Parsing. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers , Mirella Lapata, P...

    2017

  76. [85]

    Maja Popović. 2017. chrF++: words helping character n-grams. In Proceedings of the Second Conference on Machine Translation , Ondřej Bojar, Christian Buck, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, a...

    2017 doi

  77. [86]

    Nima Pourdamghani, Kevin Knight, and Ulf Hermjakob. 2016. Generating English from Abstract Meaning Representations. In Proceedings of the 9th International Natural Language Generation conference , Amy Isard, Verena Rieser, and Dimitra Gkatzia (Eds.). Association for Computatio...

    2016 doi

  78. [87]

    Haoyi Qiu, Kung-Hsiang Huang, Jingnong Qu, and Nanyun Peng. 2024. AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Lingui...

    2024 doi

  79. [88]

    Sudha Rao, Daniel Marcu, Kevin Knight, and Hal Daumé III. 2017. Biomedical Event Extraction using Abstract Meaning Representation. In BioNLP 2017, Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, and Junichi Tsujii (Eds.). Association for Computational Linguistics...

    2017 doi

  80. [89]

    Michael Regan, Shira Wein, George Baker, and Emilio Monti. 2024. MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detection. arXiv:2405.19285 [cs.CL] https://arxiv.org/abs/2405.19285

    2024 arXiv

  81. [90]

    Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP- IJ...

    2019 doi

  82. [91]

    Leonardo F. R. Ribeiro, Yue Zhang, and Iryna Gurevych. 2021. Structural Adapters in Pretrained Language Models for AMR-to-Text Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , Marie-Francine Moens, Xuanjing Huang, Lucia Sp...

    2021 doi

  83. [92]

    Zacchary Sadeddine, Juri Opitz, and Fabian Suchanek. 2024. A Survey of Meaning Representations – From Theory to Practical Utility. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (...

    2024 doi

  84. [93]

    Ziyi Shou, Yuxin Jiang, and Fangzhen Lin. 2022. AMR-DA: Data Augmentation by Abstract Meaning Representation. InFindings of the Association for Computational Linguistics: ACL 2022 , Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational ...

    2022 doi

  85. [94]

    Ziyi Shou and Fangzhen Lin. 2023. Evaluate AMR Graph Similarity via Self-supervised Learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (Eds.). Associ...

    2023 doi

  86. [95]

    Linfeng Song and Daniel Gildea. 2019. SemBleu: A Robust Metric for AMR Parsing Evaluation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , Anna Korhonen, David Traum, and Lluís Màrquez (Eds.). Association for Computational Linguisti...

    2019 doi

  87. [96]

    Linfeng Song, Daniel Gildea, Yue Zhang, Zhiguo Wang, and Jinsong Su. 2019. Semantic Neural Machine Translation Using AMR. Transactions of the Association for Computational Linguistics 7 (2019), 19–31. https://doi.org/10.1162/tacl_a_00252

    2019 doi

  88. [97]

    Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2017. AMR-to-text Generation with Synchronous Node Replacement Grammar. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , Regina Barzila...

    2017 doi

  89. [98]

    Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018. A Graph-to-Sequence Model for AMR-to-Text Generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Iryna Gurevych and Yusuke Miyao (Eds.). Ass...

    2018 doi

  90. [99]

    William Soto Martinez, Yannick Parmentier, and Claire Gardent. 2024. Generating from AMRs into High and Low-Resource Languages using Phylogenetic Knowledge and Hierarchical QLoRA Training (HQL). In Proceedings of the 17th International Natural Language Generation Conference , ...

    2024

  91. [100]

    Afonso Sousa and Henrique Cardoso. [n. d.]. SAPG: Semantically-Aware Paraphrase Generation with AMR Graphs. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART . 861–871. https://doi.org/10.5220/0013379700003890

    doi

  92. [101]

    Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, and Angela Fan. 2020. Multilingual Translation with Extensible Multilingual Pretraining and Finetuning. arXiv:2008.00401 [cs.CL] https://arxiv.org/abs/2008.00401

    2020 arXiv

  93. [102]

    Nasim Tohidi, Chitra Dadkhah, Reza Nouralizadeh Ganji, Ehsan Ghaffari Sadr, and Hoda Elmi. 2024. PAMR: Persian Abstract Meaning Representation Corpus. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 23, 3, Article 35 (mar 2024), 20 pages. https://doi.org/10.1145/3638288

    2024 doi

  94. [103]

    Sarah Uhrig, Yoalli Garcia, Juri Opitz, and Anette Frank. 2021. Translate, then Parse! A Strong Baseline for Cross-Lingual AMR Parsing. InProceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal D...

    2021 doi

  95. [104]

    Jens E. L. Van Gysel, Meagan Vigus, Jayeol Chun, Kenneth Lai, Sarah Moeller, Jiarui Yao, Tim O’Gorman, Andrew Cowell, William Croft, Chu-Ren Huang, Jan Hajič, James H. Martin, Stephan Oepen, Martha Palmer, James Pustejovsky, Rosa Vallejos, and Nianwen Xue. 2021. Designing a Un...

    2021 doi

  96. [105]

    Lucy Vanderwende, Arul Menezes, and Chris Quirk. 2015. An AMR parser for English, French, German, Spanish and Japanese and a new AMR-annotated corpus. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstr...

    2015 doi

  97. [106]

    Pavlo Vasylenko, Pere Lluís Huguet Cabot, Abelardo Carlos Martínez Lorenzo, and Roberto Navigli. 2023. Incorporating Graph Information in Transformer-based AMR Parsing. In Findings of the Association for Computational Linguistics: ACL 2023 , Anna Rogers, Jordan Boyd-Graber, an...

    2023 doi

  98. [107]

    Supriti Vijay and Daniel Hershcovich. 2024. Can Abstract Meaning Representation Facilitate Fair Legal Judgement Predictions?. In Proceedings of the Fifth Workshop on Insights from Negative Results in NLP , Shabnam Tafreshi, Arjun Akula, João Sedoc, Aleksandr Drozd, Anna Rogers...

    2024 doi

  99. [108]

    Chuan Wang, Sameer Pradhan, Xiaoman Pan, Heng Ji, and Nianwen Xue. 2016. CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) , Steven Bethard, Marine Carpuat, Daniel Cer, ...

    2016 doi

  100. [109]

    Chuan Wang, Nianwen Xue, and Sameer Pradhan. 2015. A Transition-based Algorithm for AMR Parsing. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Rada Mihalcea, Joyce Chai, and A...

    2015 doi

  101. [110]

    Tianming Wang, Xiaojun Wan, and Hanqi Jin. 2020. AMR-To-Text Generation with Graph Transformer. Transactions of the Association for Computational Linguistics 8 (2020), 19–33. https://doi.org/10.1162/tacl_a_00297

    2020 doi

  102. [111]

    Tianming Wang, Xiaojun Wan, and Shaowei Yao. 2020. Better AMR-To-Text Generation with Graph Structure Reconstruction. InProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 , Christian Bessiere (Ed.). International Joint Conferenc...

    2020 doi

  103. [112]

    Yanshan Wang, Sijia Liu, Majid Rastegar-Mojarad, Liwei Wang, Feichen Shen, Fei Liu, and Hongfang Liu. 2017. Dependency and AMR Embeddings for Drug-Drug Interaction Extraction from Biomedical Literature. In Proceedings of the 8th ACM International Conference on Bioinformatics, ...

    2017

  104. [113]

    Shira Wein and Nathan Schneider. 2021. Classifying Divergences in Cross-lingual AMR Pairs. In Proceedings of the Joint 15th Linguistic Annotation Workshop (LA W) and 3rd Designing Meaning Representations (DMR) Workshop, Claire Bonial and Nianwen Xue (Eds.). Association for Com...

    2021 doi

  105. [114]

    Shira Wein and Nathan Schneider. 2024. Assessing the Cross-linguistic Utility of Abstract Meaning Representation. Computational Linguistics (2024), 1–55

    2024

  106. [115]

    Shira Wein and Nathan Schneider. 2024. Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation. InProceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) , Yvette Grah...

    2024

  107. [116]

    Aaron Steven White, Drew Reisinger, Keisuke Sakaguchi, Tim Vieira, Sheng Zhang, Rachel Rudinger, Kyle Rawlins, and Benjamin Van Durme

  108. [117]

    Gregor Williamson, Patrick Elliott, and Yuxin Ji. 2021. Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope. InProceedings of the Joint 15th Linguistic Annotation Workshop (LA W) and 3rd Designing Meaning Representations (DMR) Workshop , Claire Bonial...

    2021 doi

  109. [118]

    Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, and Guodong Zhou. 2021. XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 1...

    2021

  110. [119]

    Runxin Xu, Peiyi Wang, Tianyu Liu, Shuang Zeng, Baobao Chang, and Zhifang Sui. 2022. A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguisti...

    2022 doi

  111. [120]

    Nianwen Xue, Ondrej Bojar, Jan Hajic, Martha Palmer, and Xiuhong Zhang. 2014. Not an Interlingua, But Close: Comparison of English AMRs to Chinese and Czech. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14) , Vol. 14. 1765–177...

    2014

  112. [121]

    Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019. AMR Parsing as Sequence-to-Graph Transduction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , Anna Korhonen, David Traum, and Lluís Màrquez (Eds.). Association for Com...

    2019 doi

  113. [122]

    Zixuan Zhang, Nikolaus Parulian, Heng Ji, Ahmed Elsayed, Skatje Myers, and Martha Palmer. 2021. Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation. InProceedings of the 59th Annual Meeting of the Associati...

    2021

  114. [123]

    Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, and Radu Florian. 2021. AMR Parsing with Action-Pointer Transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Krist...

    2021

  115. [124]

    Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Young-Suk Lee, Radu Florian, and Salim Roukos. 2021. Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing. In Proceedings of the 2021 Conference on Empirical Methods in Natural ...

    2021

  116. [125]

    Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, and Guodong Zhou. 2019. Modeling Graph Structure in Transformer for Better AMR-to-Text Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint ...

    2019 doi

  117. [2016]

    In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , Jian Su, Kevin Duh, and Xavier Carreras (Eds.)

    Universal Decompositional Semantics on Universal Dependencies. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , Jian Su, Kevin Duh, and Xavier Carreras (Eds.). Association for Computational Linguistics, Austin, Texas, 1713–1723. https...

    2016 doi

  118. [2020]

    Dialogue-AMR: Abstract Meaning Representation for Dialogue. In Proceedings of the Twelfth Language Resources and Evaluation Conference , Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Ben...

    2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.