Learning Bi-Utterance For Multi-Turn Response Selection In Retrieval-Based Chatbots

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.