Abstract Meaning Representation

• Introduction The best introduction to AMR.
• AMR website
• Banarescu et al 2013 - Abstract Meaning Representation for Sembanking
• AMR Annotation
  • AMR Dict List of linguistic phenomena and how to handle them in AMR
• AMR Bibliography
• Generation
  • Hao et al 2022 - A Survey : Neural Networks for AMR-to-Text
• Applications
  • Wein & Opizt - A Survey of AMR Applications

See an updated list of AMR papers here: AMR Bibliography

See also Semantic Parsing and Google Scholar - AMR Parsing.

• Graph-based
  • Flanigan et al 2014 - A Discriminative Graph-Based Parser for the Abstract Meaning Representation
  • Lyu & Titov 2018 - AMR Parsing as Graph Prediction with Latent Alignment
  • Zhou et al 2020 - AMR Parsing with Latent Structural Information
  • Bai et al 2022 - Graph Pre-training for AMR Parsing and Generation
• Transition-based
  • Wang et al 2015 - A Transition-based Algorithm for AMR Parsing
  • Peng et al 2017 - Addressing the Data Sparsity Issue in Neural AMR Parsing
  • Ballesteros & Al-Onaizan 2017 - AMR Parsing using Stack-LSTMs
  • Naseem et al 2019 - Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
  • Cai & Lam 2020 - AMR Parsing via Graph Sequence Iterative Inference I would classify this approach as a transition-based algorithm that incrementally builds the graph
  • Astudillo et al 2020 - Transition-based Parsing with Stack-Transformers
  • Zhou et al 2021 - AMR Parsing with Action-Pointer Transformer
  • Drozdov et al 2022 - Inducing and Using Alignments for Transition-based AMR Parsing
• Grammar-based
  • Artzi et al 2015 - Broad-coverage CCG Semantic Parsing with AMR
  • Gildea et al 2019 - Ordered Tree Decomposition for HRG Rule Extraction
• Seq2seq
  • Konstas et al 2017 - Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
  • Ge et al 2019 - Modeling Source Syntax and Semantics for Neural AMR Parsing
  • Xu et al 2020 - Improving AMR Parsing with Sequence-to-Sequence Pre-training
  • AMRBART: Bai et al 2022 - Graph Pre-training for AMR Parsing and Generation (SOTA as of March 2023)
  • BiBL: Cheng et al 2023 - BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning
• Prompted LLMs
  • Lee et al 2023 - AMR Parsing with Instruction Fine-tuned Pre-trained Language Models
• Other methods
  • Zhou et al 2016 - AMR Parsing with an Incremental Joint Model Has a joint model for concept and relation identification. Compares to the same feature set as JAMR.
  • Cai & Lam 2019 - Core Semantic First: A Top-down Approach for AMR Parsing
  • Zhang et al 2019 - Broad-Coverage Semantic Parsing as Transduction
  • Lindemann et al 2020 - Fast semantic parsing with well-typedness guarantees
• Domain Adaptation
  • Bai et al 2022 - Cross-domain Generalization for AMR Parsing

See also Generation

• Wang et al 2020 - AMR-To-Text Generation with Graph Transformer
• Hoyle et al 2020 - Promoting Graph Awareness in Linearized Graph-to-Text Generation

• Machine Translation
  • Song et al 2019 - Semantic Neural Machine Translation using AMR
  • Nguyen et al 2020 - Integrating AMR to Neural Machine Translation using Graph Attention Networks
  • Nguyen et al 2021 - Improving Neural Machine Translation with AMR Semantic Graphs
  • Li & Flanigan 2022 - Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers
  • Wein & Schneider 2023 - Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation
• Summarization
  • Liu et al 2015 - Toward Abstractive Summarization Using Semantic Representations
  • Liao et al 2018 - Abstract Meaning Representation for Multi-Document Summarization
  • Kouris et al 2022 - Text Summarization Based on Semantic Graphs: An Abstract Meaning Representation Graph-to-Text Deep Learning Approach
  • Ribeiro et al 2022 - FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
  • (See also Fei Liu's Publications)
  • Qiu et al 2024 - AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation
• Applications to (or with) LLMs
  • Jiang et al 2024 - Don’t Forget to Connect! Improving RAG with Graph-based Reranking
• Question Answering
  • Mitra & Baral 2016- Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning
  • Lim et al 2020 - I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning
  • Kapanipathi et al 2021 - Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
  • Xu et al 2021 - Exploiting Reasoning Chains for Multi-hop Science Question Answering
  • Xu et al 2021 - Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
  • Deng et al 2022 - Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering
  • Wang et al 2023 - Exploiting Abstract Meaning Representation for Open-Domain Question Answering
• Classification
  • Ma et al 2023 - AMR-based Path Aggregation Graph Network for Aspect-based Sentiment Analysis At ACL 2023
• Information Extraction
  • Pan et al 2015 - Unsupervised Entity Linking with Abstract Meaning Representation
  • Garg et al 2015 - Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
  • Rao et al 2017 - Biomedical Event Extraction using Abstract Meaning Representation
• Dialog
  • Bonial et al 2020 - Dialogue-AMR: Abstract Meaning Representation for Dialogue
  • Bai et al 2021 - Semantic Representation for Dialogue Modeling
• Prompting and LLMs
  • 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
  • Ribeiro et al 2022 - FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
• Style-Transfer
  • Jangra et al 2022 - T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
• Data Augmentation
  • 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.
• Embodied or Vision and Language
  • Choi et al 2022 - Scene Graph Parsing via Abstract Meaning Representation in Pre-trained Language Models Not really vision related. Uses AMR to help with caption to scene-graph parsing
• Pre-training and Embedding Representations
  • Opitz & Frank 2022 - SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features

• Other languages than English
  • Chinese & Czech: Xue et al 2014 - Not an Interlingua, but Close: Comparison of English AMRs to Chinese and Czech
  • Chinese: Li et al 2016 - Annotating the Little Prince with Chinese AMRs
  • Vigus 2020 - Cross-Lingual Annotation: A Road Map for Low- and No-Resource Languages
• Multi-sentence AMR
• Time and Temporal Information
  • Donatelli et al 2018 - Annotation of Tense and Aspect Semantics for Sentential AMR
• Bos 2020 - Separating Argument Structure from Logical Structure in AMR
• Quantifier Scoping and Inference
  • Williamson et al 2021 - Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope
• UMR
  • Pustejovsky et al 2019 - Modeling Quantification and Scope in Abstract Meaning Representations
  • Van Gysel et al 2021 - Designing a Uniform Meaning Representation for Natural Language Processing (pdf)
  • UMR Guidelines
• Interlingual extensions
  • BabelNet Meaning Representation (BMR): Lorenzo et al 2022 - Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation
• Bonn et al 2020 - Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus
• Dialog
  • Bonial et al 2020 - Dialogue-AMR: Abstract Meaning Representation for Dialogue
  • Bonial et al 2021 - Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains
• Lai et al 2020 - A Continuation Semantics for Abstract Meaning Representation
• Ji et al 2022 - Automatic Enrichment of Abstract Meaning Representations

• Parsing
  • Fine-grained evaluation: Damonte et al 2016 - An Incremental Parser for Abstract Meaning Representation (see section 5)
  • Song & Gildea 2019 - SemBleu: A Robust Metric for AMR Parsing Evaluation (github)
  • Opitz & Frank 2022 - Better Smatch = Better Parser? AMR evaluation is not so simple anymore
  • Opitz 2023 - SMATCH++: Standardized and Extended Evaluation of Semantic Graphs github
• Generation
  • Opitz & Frank 2020 - Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR

• Parsers
  • This one works well: Cai & Lam 2020 - AMR Parsing via Graph Sequence Iterative Inference Rongwen has been using it. Ask him if you want help running it.
• Libraries for reading AMR graphs
  • Penman — a Python library for PENMAN graph notation docs
• Alignment
  • Pourdamghani et al 2015 - Aligning English Strings with Abstract Meaning Representation Graphs software or this software This is the aligner that was used to produce the alignments in the AMR LDC data.
  • Saphra & Lopez 2015 - AMRICA: an AMR Inspector for Cross-language Alignments

Related: Coreference Resolution, Implicit Roles

• O’Gorman et al 2018 - AMR Beyond the Sentence: the Multi-sentence AMR corpus (this data is released in AMR 3.0 LDC2020T02)
  • Multisentence AMR Guidelines
• Related work: Ebner et al 2019 - Multi-Sentence Argument Linking
• Anikina et al 2020 - Predicting Coreference in Abstract Meaning Representations
• Fu et al 2021 - End-to-End AMR Coreference Resolution First paper to do document coreferenece for AMRs (not full multi-document AMRs because missing implicit roles). The input is AMR, doesn't actually use the text (github)
• Naseem et al 2022 - DOCAMR: Multi-Sentence AMR Representation and Evaluation

• Annotation Schemes
  • Brutti et al 2022 - Abstract Meaning Representation for Gesture
  • Donatelli et al 2022 - Towards Situated AMR: Creating a Corpus of Gesture AMR
• Parsers
  • Choi et al 2022 - Scene Graph Parsing via Abstract Meaning Representation in Pre-trained Language Models
  • Abdelsalam et al 2022 - Visual Semantic Parsing: From Images to Abstract Meaning Representation

• IBM AMR Parser

• Martha Palmer
• James Pustejovsky
• Linfeng Song
• Nianwen Xue

• AMR Annotation
• Graph Neural Networks
• Semantic Representations