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        <title>Macro Grammars and Holistic Triggering for Efficient Semantic Parsing</title>
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        <namePart type="given">Yuchen</namePart>
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    <abstract>To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations. We propose a new online learning algorithm that searches faster as training progresses. The two key ideas are using macro grammars to cache the abstract patterns of useful logical forms found thus far, and holistic triggering to efficiently retrieve the most relevant patterns based on sentence similarity. On the WikiTableQuestions dataset, we first expand the search space of an existing model to improve the state-of-the-art accuracy from 38.7% to 42.7%, and then use macro grammars and holistic triggering to achieve an 11x speedup and an accuracy of 43.7%.</abstract>
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%0 Conference Proceedings
%T Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
%A Zhang, Yuchen
%A Pasupat, Panupong
%A Liang, Percy
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zhang-etal-2017-macro
%X To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations. We propose a new online learning algorithm that searches faster as training progresses. The two key ideas are using macro grammars to cache the abstract patterns of useful logical forms found thus far, and holistic triggering to efficiently retrieve the most relevant patterns based on sentence similarity. On the WikiTableQuestions dataset, we first expand the search space of an existing model to improve the state-of-the-art accuracy from 38.7% to 42.7%, and then use macro grammars and holistic triggering to achieve an 11x speedup and an accuracy of 43.7%.
%R 10.18653/v1/D17-1125
%U https://aclanthology.org/D17-1125
%U https://doi.org/10.18653/v1/D17-1125
%P 1214-1223

Markdown (Informal)

[Macro Grammars and Holistic Triggering for Efficient Semantic Parsing](https://aclanthology.org/D17-1125) (Zhang et al., EMNLP 2017)

• Macro Grammars and Holistic Triggering for Efficient Semantic Parsing (Zhang et al., EMNLP 2017)

ACL

• Yuchen Zhang, Panupong Pasupat, and Percy Liang. 2017. Macro Grammars and Holistic Triggering for Efficient Semantic Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1214–1223, Copenhagen, Denmark. Association for Computational Linguistics.