  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.
