Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 3774–3785, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 3774 Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs Bailin Wang, Ivan Titov and Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh bailin.wang@ed.ac.uk, {ititov,mlap}@inf.ed.ac.uk Abstract Semantic parsing aims to map natural lan- guage utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. Weakly-supervised semantic parsers are trained on utterance- denotation pairs treating programs as latent. The task is challenging due to the large search space and spuriousness of programs which may execute to the correct answer but do not generalize to unseen examples. Our goal is to instill an inductive bias in the parser to help it distinguish between spurious and cor- rect programs. We capitalize on the intu- ition that correct programs would likely re- spect certain structural constraints were they to be aligned to the question (e.g., program fragments are unlikely to align to overlapping text spans) and propose to model alignments as structured latent variables. In order to make the latent-alignment framework tractable, we decompose the parsing task into (1) predicting a partial “abstract program” and (2) refining it while modeling structured alignments with differential dynamic programming. We ob- tain state-of-the-art performance on the WIK- ITABLEQUESTIONS and WIKISQL datasets. When compared to a standard attention base- line, we observe that the proposed structured- alignment mechanism is highly beneficial. 1 Introduction Semantic parsing is the task of translating natural language to machine interpretable meaning repre- sentations. Typically, it requires mapping a natural language utterance onto a program, which is exe- cuted against a knowledge base to obtain an an- swer or a denotation. Most previous work (Zettle- moyer and Collins, 2005; Wong and Mooney, 2007; Lu et al., 2008; Jia and Liang, 2016) has focused on the supervised setting where a model is learned from question-program pairs. Weakly Question: ��� ���� ������ ������ ��� ��� ������ �� ������ ���� Abstract Program: ������� ���������� ������������ � Correct2Program: ������� �������������� � �������� ������������� � Instantiation Execution Denotation: 0 Spurious Programs: ������������������������� ����������� ������������� �� ������������� �� ������� ������� ����������� ������������� �� ������������� � Inconsistent Program: ������� �������������� � �������� �������������� Figure 1: After generating an abstract program for a question, our parser finds alignments between slots (with prefix #) and question spans. Based on the align- ment, it instantiates each slot and a complete program is executed against a table to obtain a denotation. supervised semantic parsing (Berant et al., 2013; Liang et al., 2011) reduces the burden of anno- tating programs by learning from questions paired with their answers (or denotations). Two major challenges arise when learning from denotations: 1) training of the semantic parser re- quires exploring a large search space of possible programs to find those which are consistent, and execute to correct denotations; …

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