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nlp:abstract_meaning_representation [2023/12/10 05:56] – [Multi-sentence AMR] jmflanignlp:abstract_meaning_representation [2025/10/17 21:56] (current) – [Evaluation] jmflanig
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   * **[[https://github.com/amrisi/amr-guidelines/blob/master/amr.md#part-i-introduction|Introduction]]** The best introduction to AMR.   * **[[https://github.com/amrisi/amr-guidelines/blob/master/amr.md#part-i-introduction|Introduction]]** The best introduction to AMR.
   * [[https://amr.isi.edu/index.html|AMR website]]   * [[https://amr.isi.edu/index.html|AMR website]]
-  * [[https://amr.isi.edu/a.pdf|Banarescu et al 2013 - Abstract Meaning Representation for Sembanking]]+  * [[https://aclanthology.org/W13-2322.pdf|Banarescu et al 2013 - Abstract Meaning Representation for Sembanking]]
   * [[AMR Annotation]]   * [[AMR Annotation]]
     * [[https://www.isi.edu/~ulf/amr/lib/amr-dict.html|AMR Dict]] List of linguistic phenomena and how to handle them in AMR     * [[https://www.isi.edu/~ulf/amr/lib/amr-dict.html|AMR Dict]] List of linguistic phenomena and how to handle them in AMR
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   * Generation   * Generation
     * [[https://arxiv.org/pdf/2206.07328.pdf|Hao et al 2022 - A Survey : Neural Networks for AMR-to-Text]]     * [[https://arxiv.org/pdf/2206.07328.pdf|Hao et al 2022 - A Survey : Neural Networks for AMR-to-Text]]
 +  * **Applications**
 +    * [[https://aclanthology.org/2024.emnlp-main.390.pdf|Wein & Opizt - A Survey of AMR Applications]]
  
 ===== Papers ===== ===== Papers =====
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     * [[https://aclanthology.org/2020.findings-emnlp.89.pdf|Astudillo et al 2020 - Transition-based Parsing with Stack-Transformers]]     * [[https://aclanthology.org/2020.findings-emnlp.89.pdf|Astudillo et al 2020 - Transition-based Parsing with Stack-Transformers]]
     * [[https://aclanthology.org/2021.naacl-main.443.pdf|Zhou et al 2021 - AMR Parsing with Action-Pointer Transformer]]     * [[https://aclanthology.org/2021.naacl-main.443.pdf|Zhou et al 2021 - AMR Parsing with Action-Pointer Transformer]]
 +    * [[https://aclanthology.org/2022.naacl-main.80.pdf|Drozdov et al 2022 - Inducing and Using Alignments for Transition-based AMR Parsing]]
   * Grammar-based   * Grammar-based
     * [[https://www.aclweb.org/anthology/D15-1198.pdf|Artzi et al 2015 - Broad-coverage CCG Semantic Parsing with AMR]]     * [[https://www.aclweb.org/anthology/D15-1198.pdf|Artzi et al 2015 - Broad-coverage CCG Semantic Parsing with AMR]]
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     * [[https://www.aclweb.org/anthology/2020.emnlp-main.196.pdf|Xu et al 2020 - Improving AMR Parsing with Sequence-to-Sequence Pre-training]]     * [[https://www.aclweb.org/anthology/2020.emnlp-main.196.pdf|Xu et al 2020 - Improving AMR Parsing with Sequence-to-Sequence Pre-training]]
     * AMRBART: [[https://arxiv.org/pdf/2203.07836.pdf|Bai et al 2022 - Graph Pre-training for AMR Parsing and Generation]] (SOTA as of March 2023)     * AMRBART: [[https://arxiv.org/pdf/2203.07836.pdf|Bai et al 2022 - Graph Pre-training for AMR Parsing and Generation]] (SOTA as of March 2023)
 +    * BiBL: [[https://aclanthology.org/2022.coling-1.485.pdf|Cheng et al 2023 - BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning]]
   * Prompted LLMs   * Prompted LLMs
     * [[https://arxiv.org/pdf/2304.12272.pdf|Lee et al 2023 - AMR Parsing with Instruction Fine-tuned Pre-trained Language Models]]     * [[https://arxiv.org/pdf/2304.12272.pdf|Lee et al 2023 - AMR Parsing with Instruction Fine-tuned Pre-trained Language Models]]
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     * [[https://ieeexplore.ieee.org/iel7/9335828/9335829/09335896.pdf|Nguyen et al 2020 - Integrating AMR to Neural Machine Translation using Graph Attention Networks]]     * [[https://ieeexplore.ieee.org/iel7/9335828/9335829/09335896.pdf|Nguyen et al 2020 - Integrating AMR to Neural Machine Translation using Graph Attention Networks]]
     * [[https://downloads.hindawi.com/journals/mpe/2021/9939389.pdf|Nguyen et al 2021 - Improving Neural Machine Translation with AMR Semantic Graphs]]     * [[https://downloads.hindawi.com/journals/mpe/2021/9939389.pdf|Nguyen et al 2021 - Improving Neural Machine Translation with AMR Semantic Graphs]]
 +    * [[https://aclanthology.org/2022.dlg4nlp-1.2.pdf|Li & Flanigan 2022 - Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers]]
 +    * [[https://arxiv.org/pdf/2304.11501|Wein & Schneider 2023 - Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation]]
   * Summarization   * Summarization
     * [[https://aclanthology.org/N15-1114.pdf|Liu et al 2015 - Toward Abstractive Summarization Using Semantic Representations]]     * [[https://aclanthology.org/N15-1114.pdf|Liu et al 2015 - Toward Abstractive Summarization Using Semantic Representations]]
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     * [[https://aclanthology.org/2022.naacl-main.236.pdf|Ribeiro et al 2022 - FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations]]     * [[https://aclanthology.org/2022.naacl-main.236.pdf|Ribeiro et al 2022 - FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations]]
     * (See also [[https://scholar.google.com/citations?user=22ohn6AAAAAJ&hl=en|Fei Liu's Publications]])     * (See also [[https://scholar.google.com/citations?user=22ohn6AAAAAJ&hl=en|Fei Liu's Publications]])
 +    * [[https://arxiv.org/pdf/2311.09521|Qiu et al 2024 - AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation]]
 +  * Applications to (or with) LLMs
 +    * [[https://arxiv.org/pdf/2405.18414|Jiang et al 2024 - Don’t Forget to Connect! Improving RAG with Graph-based Reranking]]
   * Question Answering   * Question Answering
     * [[http://www.public.asu.edu/~cbaral/papers/aaai2016-sub.pdf|Mitra & Baral 2016-  Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning]]     * [[http://www.public.asu.edu/~cbaral/papers/aaai2016-sub.pdf|Mitra & Baral 2016-  Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning]]
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     * [[https://aclanthology.org/2021.findings-acl.90.pdf|Xu et al 2021 - Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering]]     * [[https://aclanthology.org/2021.findings-acl.90.pdf|Xu et al 2021 - Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering]]
     * [[https://arxiv.org/pdf/2206.08486.pdf|Deng et al 2022 - Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering]]     * [[https://arxiv.org/pdf/2206.08486.pdf|Deng et al 2022 - Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering]]
 +    * [[https://arxiv.org/pdf/2305.17050|Wang et al 2023 - Exploiting Abstract Meaning Representation for Open-Domain Question Answering]]
   * Classification   * Classification
     * [[https://openreview.net/pdf?id=dhf_LF2WDQS|Ma et al 2023 - AMR-based Path Aggregation Graph Network for Aspect-based Sentiment Analysis]] At ACL 2023     * [[https://openreview.net/pdf?id=dhf_LF2WDQS|Ma et al 2023 - AMR-based Path Aggregation Graph Network for Aspect-based Sentiment Analysis]] At ACL 2023
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     * [[https://www.aclweb.org/anthology/2020.lrec-1.86.pdf|Bonial et al 2020 - Dialogue-AMR: Abstract Meaning Representation for Dialogue]]     * [[https://www.aclweb.org/anthology/2020.lrec-1.86.pdf|Bonial et al 2020 - Dialogue-AMR: Abstract Meaning Representation for Dialogue]]
     * [[https://arxiv.org/pdf/2105.10188.pdf|Bai et al 2021 - Semantic Representation for Dialogue Modeling]]     * [[https://arxiv.org/pdf/2105.10188.pdf|Bai et al 2021 - Semantic Representation for Dialogue Modeling]]
 +  * Prompting and LLMs
 +    * [[https://arxiv.org/pdf/2405.01502|Jin et al 2024 - Analyzing the Role of Semantic Representations in the Era of Large Language Models]] Uses AMR in a CoT-style prompt
   * Fact-Checking   * Fact-Checking
     * [[https://aclanthology.org/2022.naacl-main.236.pdf|Ribeiro et al 2022 - FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations]]     * [[https://aclanthology.org/2022.naacl-main.236.pdf|Ribeiro et al 2022 - FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations]]
 +  * Style-Transfer
 +    * [[https://arxiv.org/pdf/2212.01667|Jangra et al 2022 - T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation]]
   * Data Augmentation   * Data Augmentation
     * [[https://aclanthology.org/2022.findings-acl.244.pdf|Shou et al 2022 - AMR-DA: Data Augmentation by Abstract Meaning Representation]] Augments the data by parsing with AMR parser, manipulating the graph, and generating a new sentence.  They used it for textual similarity task.     * [[https://aclanthology.org/2022.findings-acl.244.pdf|Shou et al 2022 - AMR-DA: Data Augmentation by Abstract Meaning Representation]] Augments the data by parsing with AMR parser, manipulating the graph, and generating a new sentence.  They used it for textual similarity task.
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 ==== Evaluation ==== ==== Evaluation ====
   * Parsing   * Parsing
 +    * Fine-grained evaluation: [[https://arxiv.org/pdf/1608.06111|Damonte et al 2016 - An Incremental Parser for Abstract Meaning Representation]] (see section 5)
     * [[https://arxiv.org/pdf/1905.10726.pdf|Song & Gildea 2019 - SemBleu: A Robust Metric for AMR Parsing Evaluation]] ([[https://github.com/freesunshine0316/sembleu|github]])     * [[https://arxiv.org/pdf/1905.10726.pdf|Song & Gildea 2019 - SemBleu: A Robust Metric for AMR Parsing Evaluation]] ([[https://github.com/freesunshine0316/sembleu|github]])
 +    * [[https://arxiv.org/pdf/2210.06461|Opitz & Frank 2022 - Better Smatch = Better Parser? AMR evaluation is not so simple anymore]]
 +    * [[https://arxiv.org/pdf/2305.06993|Opitz 2023 - SMATCH++: Standardized and Extended Evaluation of Semantic Graphs]] [[https://github.com/flipz357/smatchpp|github]]
   * Generation   * Generation
     * [[https://arxiv.org/pdf/2008.08896.pdf|Opitz & Frank 2020 - Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR]]     * [[https://arxiv.org/pdf/2008.08896.pdf|Opitz & Frank 2020 - Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR]]
nlp/abstract_meaning_representation.1702187773.txt.gz · Last modified: 2023/12/10 05:56 by jmflanig
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