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        <title>Active Imitation Learning with Noisy Guidance</title>
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        <namePart type="given">Kianté</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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            <namePart type="given">Natalie</namePart>
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    <abstract>Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies. Such algorithms assume training-time access to an expert that can provide the optimal action at any queried state; unfortunately, the number of such queries is often prohibitive, frequently rendering these approaches impractical. To combat this query complexity, we consider an active learning setting in which the learning algorithm has additional access to a much cheaper noisy heuristic that provides noisy guidance. Our algorithm, LEAQI, learns a difference classifier that predicts when the expert is likely to disagree with the heuristic, and queries the expert only when necessary. We apply LEAQI to three sequence labelling tasks, demonstrating significantly fewer queries to the expert and comparable (or better) accuracies over a passive approach.</abstract>
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        <date>2020-07</date>
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            <start>2093</start>
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%0 Conference Proceedings
%T Active Imitation Learning with Noisy Guidance
%A Brantley, Kianté
%A Sharaf, Amr
%A Daumé III, Hal
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F brantley-etal-2020-active
%X Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies. Such algorithms assume training-time access to an expert that can provide the optimal action at any queried state; unfortunately, the number of such queries is often prohibitive, frequently rendering these approaches impractical. To combat this query complexity, we consider an active learning setting in which the learning algorithm has additional access to a much cheaper noisy heuristic that provides noisy guidance. Our algorithm, LEAQI, learns a difference classifier that predicts when the expert is likely to disagree with the heuristic, and queries the expert only when necessary. We apply LEAQI to three sequence labelling tasks, demonstrating significantly fewer queries to the expert and comparable (or better) accuracies over a passive approach.
%R 10.18653/v1/2020.acl-main.189
%U https://aclanthology.org/2020.acl-main.189
%U https://doi.org/10.18653/v1/2020.acl-main.189
%P 2093-2105

Markdown (Informal)

[Active Imitation Learning with Noisy Guidance](https://aclanthology.org/2020.acl-main.189) (Brantley et al., ACL 2020)

• Active Imitation Learning with Noisy Guidance (Brantley et al., ACL 2020)

ACL

• Kianté Brantley, Amr Sharaf, and Hal Daumé III. 2020. Active Imitation Learning with Noisy Guidance. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2093–2105, Online. Association for Computational Linguistics.