Graph-Based Decoding for Task Oriented Semantic Parsing
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Abstract (arXiv)
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 9 Sep 2021.
Builds on
22 of 34 references.
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10 more
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.