The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leverage target information for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
@article{arxiv.2011.07432,
title = {Target Guided Emotion Aware Chat Machine},
author = {Wei Wei and Jiayi Liu and Xianling Mao and Guibin Guo and Feida Zhu and Pan Zhou and Yuchong Hu and Shanshan Feng},
journal= {arXiv preprint arXiv:2011.07432},
year = {2021}
}