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.
@article{arxiv.1701.02073,
title = {Neural Personalized Response Generation as Domain Adaptation},
author = {Weinan Zhang and Ting Liu and Yifa Wang and Qingfu Zhu},
journal= {arXiv preprint arXiv:1701.02073},
year = {2019}
}