%0 Journal Article
%T Learning bi
%A Dapeng Li
%A Jing Geng
%A Longxing Yang
%A Shuliang Wang
%A Tianru Dai
%J International Journal of Advanced Robotic Systems
%@ 1729-8814
%D 2019
%R 10.1177/1729881419841930
%X 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
%K Learning bi-utterance
%K dialogue system
%K deep learning
%K information retrieval
%U https://journals.sagepub.com/doi/full/10.1177/1729881419841930