Compositional Task-Oriented Parsing as Abstractive Question Answering

• URL: http://arxiv.org/abs/2205.02068v1
• Date: Wed, 4 May 2022 14:01:08 GMT
• Title: Compositional Task-Oriented Parsing as Abstractive Question Answering
• Authors: Wenting Zhao, Konstantine Arkoudas, Weiqi Sun, and Claire Cardie
• Abstract summary: Task-oriented parsing aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. A popular approach to TOP is to apply seq2seq models to generate linearized parse trees. A more recent line of work argues that pretrained seq2seq models are better at generating outputs that are themselves natural language, so they replace linearized parse trees with canonical natural-language paraphrases.
• Score: 25.682923914685063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. A popular approach to TOP is to apply seq2seq models to generate linearized parse trees. A more recent line of work argues that pretrained seq2seq models are better at generating outputs that are themselves natural language, so they replace linearized parse trees with canonical natural-language paraphrases that can then be easily translated into parse trees, resulting in so-called naturalized parsers. In this work we continue to explore naturalized semantic parsing by presenting a general reduction of TOP to abstractive question answering that overcomes some limitations of canonical paraphrasing. Experimental results show that our QA-based technique outperforms state-of-the-art methods in full-data settings while achieving dramatic improvements in few-shot settings.

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