From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction

Zihang Dai; Qizhe Xie; Eduard Hovy

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arXiv · 1804.10974

From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction

Zihang Dai·Qizhe Xie·Eduard Hovy

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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.

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Zihang Dai, Qizhe Xie, Eduard Hovy. 2018-04-29. From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction. https://arxiv.org/abs/1804.10974

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