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Semantic parsing aims at mapping natural language to machine interpretablemeaning representations. Traditional approaches rely on high-quality lexicons,manually-built templates, and linguistic features which are either domain- orrepresentation-specific. In this paper we present a general method based on anattention-enhanced encoder-decoder model. We encode input utterances intovector representations, and generate their logical forms by conditioning theoutput sequences or trees on the encoding vectors. Experimental results on fourdatasets show that our approach performs competitively without usinghand-engineered features and is easy to adapt across domains and meaningrepresentations.

Language to Logical Form with Neural Attention