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        <title>Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback</title>
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    <name type="personal">
        <namePart type="given">Carolin</namePart>
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    <name type="personal">
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies in a proper reweighting of the estimator so as to avoid known degeneracies in counterfactual learning, while still being applicable to stochastic gradient optimization. To conduct experiments with human users, we devise an easy-to-use interface to collect human feedback on semantic parses. Our work is the first to show that semantic parsers can be improved significantly by counterfactual learning from logged human feedback data.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1169</identifier>
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%0 Conference Proceedings
%T Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback
%A Lawrence, Carolin
%A Riezler, Stefan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F lawrence-riezler-2018-improving
%X Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies in a proper reweighting of the estimator so as to avoid known degeneracies in counterfactual learning, while still being applicable to stochastic gradient optimization. To conduct experiments with human users, we devise an easy-to-use interface to collect human feedback on semantic parses. Our work is the first to show that semantic parsers can be improved significantly by counterfactual learning from logged human feedback data.
%R 10.18653/v1/P18-1169
%U https://aclanthology.org/P18-1169
%U https://doi.org/10.18653/v1/P18-1169
%P 1820-1830

Markdown (Informal)

[Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback](https://aclanthology.org/P18-1169) (Lawrence & Riezler, ACL 2018)

• Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback (Lawrence & Riezler, ACL 2018)

ACL

• Carolin Lawrence and Stefan Riezler. 2018. Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1820–1830, Melbourne, Australia. Association for Computational Linguistics.