Predicting Question Responses to Improve the Performance of Retrieval-Based Chatbot

Abstract

Chatbot models are built to mimic a conversation between humans and fulfill different tasks. Retrieval-based chatbot models are designed to select the most appropriate response from a pool of candidates given a past conversation and current input. During the conversation, chatbots are expected to (1) provide direct assistant when the user request is clear or (2) ask clarification questions to gather more information to better understand the user’s need. Despite its importance, few studies have looked at when to ask questions and how to retrieve relevant questions accordingly. As a result, existing retrieval-based chatbot models perform poorly when the correct response is a question. To overcome this limitation, we propose an adaptive response retrieval model. Specifically, we first predict whether the best response should be a question, and then apply different models to retrieve the responses accordingly. A novel question response retrieval model is proposed to better capture the matching patterns between question responses with the conversations. Experiments on two public data sets show the proposed adaptive model can significantly and consistently improve the retrieval performance in particular for the question responses.