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Neural Personalized Response Generation As Domain Adaptation

Weinan Zhang, Ting Liu, Yifa Wang, Qingfu Zhu . World Wide Web 2018 – 92 citations

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Evaluation Fine Tuning Reinforcement Learning Tools

In this paper, we focus on the personalized response generation for conversational systems. Based on the sequence to sequence learning, especially the encoder-decoder framework, we propose a two-phase approach, namely initialization then adaptation, to model the responding style of human and then generate personalized responses. For evaluation, we propose a novel human aided method to evaluate the performance of the personalized response generation models by online real-time conversation and offline human judgement. Moreover, the lexical divergence of the responses generated by the 5 personalized models indicates that the proposed two-phase approach achieves good results on modeling the responding style of human and generating personalized responses for the conversational systems.

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