Deep Reinforcement Learning for Dialogue Generation

EMNLP 2016 Jiwei LiWill MonroeAlan RitterMichel GalleyJianfeng GaoDan Jurafsky

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning... (read more)

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