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        <title>From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction</title>
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        <namePart type="given">Zihang</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
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    <abstract>In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction.</abstract>
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%0 Conference Proceedings
%T From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction
%A Dai, Zihang
%A Xie, Qizhe
%A Hovy, Eduard
%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 dai-etal-2018-credit
%X In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction.
%R 10.18653/v1/P18-1155
%U https://aclanthology.org/P18-1155
%U https://doi.org/10.18653/v1/P18-1155
%P 1672-1682

Markdown (Informal)

[From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction](https://aclanthology.org/P18-1155) (Dai et al., ACL 2018)

• From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction (Dai et al., ACL 2018)

ACL

• Zihang Dai, Qizhe Xie, and Eduard Hovy. 2018. From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1672–1682, Melbourne, Australia. Association for Computational Linguistics.