Graph-Based Decoding for Task Oriented Semantic Parsing

• URL: http://arxiv.org/abs/2109.04587v1
• Date: Thu, 9 Sep 2021 23:22:09 GMT
• Title: Graph-Based Decoding for Task Oriented Semantic Parsing
• Authors: Jeremy R. Cole, Nanjiang Jiang, Panupong Pasupat, Luheng He, Peter Shaw
• Abstract summary: We formulate semantic parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. 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.
• Score: 16.054030490095464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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