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        <title>Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations</title>
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        <namePart type="given">Dipendra</namePart>
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    <name type="personal">
        <namePart type="given">Ming-Wei</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.</abstract>
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%0 Conference Proceedings
%T Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
%A Misra, Dipendra
%A Chang, Ming-Wei
%A He, Xiaodong
%A Yih, Wen-tau
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F misra-etal-2018-policy
%X Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.
%R 10.18653/v1/D18-1266
%U https://aclanthology.org/D18-1266
%U https://doi.org/10.18653/v1/D18-1266
%P 2442-2452

Markdown (Informal)

[Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations](https://aclanthology.org/D18-1266) (Misra et al., EMNLP 2018)

• Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations (Misra et al., EMNLP 2018)

ACL

• Dipendra Misra, Ming-Wei Chang, Xiaodong He, and Wen-tau Yih. 2018. Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2442–2452, Brussels, Belgium. Association for Computational Linguistics.