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arXiv · 1704.08387

Learning Structured Natural Language Representations for Semantic Parsing

Jianpeng Cheng·Siva Reddy·Vijay Saraswat·Mirella Lapata
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Abstract

We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We obtain competitive results on various datasets. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.

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Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata. 2017-04-27. Learning Structured Natural Language Representations for Semantic Parsing. https://arxiv.org/abs/1704.08387

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