An Adaptive Response Matching Network for Ranking Multi-turn Chatbot Responses

With the increasing popularity of personal assistant systems, it is crucial to build a chatbot that can communicate with humans and assist them to complete different tasks. A fundamental problem that any chatbots need to address is how to rank candidate responses based on previous utterances in a multi-turn conversation. A previous utterance could be either a past input from the user or a past response from the chatbot. Intuitively, a correct response needs to match well with both past responses and past inputs, but in a different way. Moreover, the matching process should depend on not only the content of the utterances but also domain knowledge. Although various models have been proposed for response matching, few of them studied how to adapt the matching mechanism to utterance types and domain knowledge. To address this limitation, this paper proposes an adaptive response matching network (ARM) to better model the matching relationship in multi-turn conversations. Specifically, the ARM model has separate response matching encoders to adapt to different matching patterns required by different utterance types. It also has a knowledge embedding component to inject domain-specific knowledge in the matching process. Experiments over two public data sets show that the proposed ARM model can significantly outperform the state of the art methods with much fewer parameters.