Responsive and Self-Expressive Dialogue Generation

Kozo Chikai, Junya Takayama, Yuki Arase
2019 Proceedings of the First Workshop on NLP for Conversational AI   unpublished
Generic responses frequently generated by neural models are a critical problem for user engagement in dialogue systems. For a more engaging chit-chat experience, we propose a response generation model motivated by the interpersonal process model for intimacy. It generates responsive and self-expressive replies, which are implemented as domainawareness and sentiment-richness, respectively. Experiments empirically confirmed that our model outperformed the sequenceto-sequence model; 68.1% of our
more » ... del; 68.1% of our responses were domain-aware with sentiment polarities, which was only 2.7% for responses generated by the sequence-to-sequence model.
doi:10.18653/v1/w19-4116 fatcat:bp6rpnf4xjcttpr46i542pid74