Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model
This addresses the challenge of making chit-chat dialogue systems more engaging and specific for users, though it appears incremental as it builds on existing sequence-to-sequence frameworks.
The paper tackles the problem of neural conversation models producing generic responses by proposing a self-attention method to generate more message-relevant and diverse responses, showing improvements in various evaluation metrics.
Using a sequence-to-sequence framework, many neural conversation models for chit-chat succeed in naturalness of the response. Nevertheless, the neural conversation models tend to give generic responses which are not specific to given messages, and it still remains as a challenge. To alleviate the tendency, we propose a method to promote message-relevant and diverse responses for neural conversation model by using self-attention, which is time-efficient as well as effective. Furthermore, we present an investigation of why and how effective self-attention is in deep comparison with the standard dialogue generation. The experiment results show that the proposed method improves the standard dialogue generation in various evaluation metrics.