Policy gradient algorithms have found wide adoption in NLP, but have recently become subject to criticism, doubting their suitability for NMT. Choshen et al. (2020) identify multiple weaknesses and suspect that their success is determined by the shape of output distributions rather than the reward. In this paper, we revisit these claims and study them under a wider range of configurations. Our experiments on in-domain and cross-domain adaptation reveal the importance of exploration and reward scaling, and provide empirical counter-evidence to these claims.

Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation



<i>**Session Chair: ** Lei Yu </i><br><br>

5C-Oral: Machine Learning for NLP: Language Modeling and Sequence to Sequence Models

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation

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