Learning bi-utterance for multi-turn response selection in retrieval-based chatbots. (16th April 2019)

• Record Type: Journal Article
• Authors: Wang, Shuliang; Li, Dapeng; Geng, Jing; Yang, Longxing; Dai, Tianru
• Is Part Of: International journal of advanced robotic systems. Volume 16:Number 2(2019:Mar./Apr.)
• Publication Date: 2019-04-16
• Subjects: Learning bi-utterance -- dialogue system -- deep learning -- information retrieval; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881419841930
• Languages: English
• ISSNs: 1729-8806

Abstract

Multi-turn response selection is essential to retrieval-based chatbots. The task requires multi-turn response selection model to match a response candidate with a conversation context. Existing methods may lose relationship features in the context. In this article, we propose an improved method that extends the learning granularity of the multi-turn response selection model to enhance the model's ability to learn relationship features of utterances in the context, which is a key to understand a conversation context for multi-turn response selection in retrieval-based chatbots. The experimental results show that our proposed method significantly improves sequential matching network for multi-turn response selection in retrieval-based chatbots.