A Transition-based Algorithm for Unrestricted AMR Parsing
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Abstract (arXiv)
Non-projective parsing can be useful to handle cycles and reentrancy in AMR graphs. We explore this idea and introduce a greedy left-to-right non-projective transition-based parser. At each parsing configuration, an oracle decides whether to create a concept or whether to connect a pair of existing concepts. The algorithm handles reentrancy and arbitrary cycles natively, i.e. within the transition system itself. The model is evaluated on the LDC2015E86 corpus, obtaining results close to the state of the art, including a Smatch of 64%, and showing good behavior on reentrant edges.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 May 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1.
4 papers.
- 2021AMR Parsing with Action-Pointer Transformercited by 25
- 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
- 2022Cross-domain Generalization for AMR Parsingcited by 2
- 2022Neural Transition-based Parsing of Library Deprecationscited by 0
- 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation26 May 2023
- 2022Neural Transition-based Parsing of Library Deprecations23 Dec 2022
- 2022Cross-domain Generalization for AMR Parsing22 Oct 2022
- 2021AMR Parsing with Action-Pointer Transformer29 Apr 2021