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.
International Journal of Advanced Robotic Systems