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Strictly Breadth-First AMR Parsing

arXiv 2211.03922 ↗First posted 8 Nov 2022cs.CLcited by 0

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Abstract (arXiv)

AMR parsing is the task that maps a sentence to an AMR semantic graph automatically. We focus on the breadth-first strategy of this task, which was proposed recently and achieved better performance than other strategies. However, current models under this strategy only encourage the model to produce the AMR graph in breadth-first order, but cannot guarantee this. To solve this problem, we propose a new architecture that guarantees that the parsing will strictly follow the breadth-first order. In each parsing step, we introduce a focused parent vertex and use this vertex to guide the generation. With the help of this new architecture and some other improvements in the sentence and graph encoder, our model obtains better performance on both the AMR 1.0 and 2.0 dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 8 Nov 2022.

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17 of 26 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2017Neural AMR: Sequence-to-Sequence Models for Parsing and Generationcited by 56
  4. 2019AMR Parsing as Sequence-to-Graph Transductioncited by 50
  5. 2020AMR Parsing via Graph-Sequence Iterative Inferencecited by 47
  6. 2018Toward Abstractive Summarization Using Semantic Representationscited by 43
  7. 2018AMR Parsing as Graph Prediction with Latent Alignmentcited by 36
  8. 2016An Incremental Parser for Abstract Meaning Representationcited by 34
  9. 2019Broad-Coverage Semantic Parsing as Transductioncited by 32
  10. 2019Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learningcited by 25
  11. 2018Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representationcited by 24
  12. 2019Core Semantic First: A Top-down Approach for AMR Parsingcited by 24
5 more
  1. 2017Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representationscited by 18
  2. 2018AMR Dependency Parsing with a Typed Semantic Algebracited by 17
  3. 2017AMR Parsing using Stack-LSTMscited by 15
  4. 2019Compositional Semantic Parsing Across Graphbankscited by 15
  5. 2017Addressing the Data Sparsity Issue in Neural AMR Parsingcited by 11

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