Target-guided emotion-aware chat machine

• Main Authors: WEI, Wei, LIU, Jiayi, MAO, Xianling, GUO, Guibing, ZHU, Feida, ZHOU, Pan, HU, Yuchong, FENG, Shanshan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Dialogue generation; emotional conversation; emotional chatbot; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6721; https://ink.library.smu.edu.sg/context/sis_research/article/7724/viewcontent/3456414.pdf
• Institution: Singapore Management University

The consistency of a response to a given post at the semantic level and emotional level is essential for a dialogue system to deliver humanlike 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 and proposes a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leveraging target information to generate 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.