Response Selection with Topic Clues for Retrieval-based Chatbots
This work addresses the challenge of generating more relevant and content-rich responses for chatbot users, though it is incremental as it builds on existing neural matching techniques.
The paper tackles the problem of improving response selection in retrieval-based chatbots by incorporating topic information, resulting in a model that significantly outperforms state-of-the-art methods on public and human-annotated datasets.
We consider incorporating topic information into message-response matching to boost responses with rich content in retrieval-based chatbots. To this end, we propose a topic-aware convolutional neural tensor network (TACNTN). In TACNTN, matching between a message and a response is not only conducted between a message vector and a response vector generated by convolutional neural networks, but also leverages extra topic information encoded in two topic vectors. The two topic vectors are linear combinations of topic words of the message and the response respectively, where the topic words are obtained from a pre-trained LDA model and their weights are determined by themselves as well as the message vector and the response vector. The message vector, the response vector, and the two topic vectors are fed to neural tensors to calculate a matching score. Empirical study on a public data set and a human annotated data set shows that TACNTN can significantly outperform state-of-the-art methods for message-response matching.