einforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedbac



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Introduction
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Bandit Machine Translation {#sec:problem}
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Effective Algorithm for Bandit MT {#sec:method}
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Modeling Imperfect Ratings {#sec:noise_model}
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Experimental Setup {#sec:experiment}
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Results and Analysis {#sec:results}
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Related Work and Discussion {#sec:related}
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Acknowledgements {#acknowledgements .unnumbered}
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Many thanks to Yvette Graham for her help with the WMT human evaluations data. We thank UMD CLIP lab members for useful discussions that led to the ideas of this paper. We also thank the anonymous reviewers for their thorough and insightful comments. This work was supported by NSF grants IIS-1320538. Boyd-Graber is also partially supported by NSF grants IIS- 1409287, IIS-1564275, IIS-IIS-1652666, and NCSE-1422492. Daum<span>é</span> III is also supported by NSF grant IIS-1618193, as well as an Amazon Research Award. Any opinions, findings, conclusions, or recommendations expressed here are those of the authors and do not necessarily reflect the view of the sponsor(s).

Neural MT Architecture
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{'source': 'arXiv_out/1707'}