Learning Structured Natural Language Representations for Semantic Parsing
This work addresses semantic parsing for natural language processing, but it is incremental as it builds on existing methods with a focus on structured representations.
The authors tackled the problem of converting natural language to logical forms by introducing a neural semantic parser that uses induced predicate-argument structures as intermediate representations, achieving competitive results on various datasets.
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.