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A fundamental challenge in developing semantic parsers is the paucity ofstrong supervision in the form of language utterances annotated with logicalform. In this paper, we propose to exploit structural regularities in languagein different domains, and train semantic parsers over multiple knowledge-bases(KBs), while sharing information across datasets. We find that we cansubstantially improve parsing accuracy by training a singlesequence-to-sequence model over multiple KBs, when providing an encoding of thedomain at decoding time. Our model achieves state-of-the-art performance on theOvernight dataset (containing eight domains), improves performance over asingle KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in thenumber of model parameters.

Neural Semantic Parsing over Multiple Knowledge-bases