Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
This work addresses the problem of data scarcity for speaker-role adaptation in neural conversation models, offering a simple and implementable solution that is incremental in nature.
The paper tackles the challenge of building persona-based conversation agents with limited speaker-specific data by proposing a multi-task learning approach that leverages cross-speaker conversation data and other speaker-related information, resulting in significant improvements in model quality and more precise capture of speaker traits and styles.
Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers' traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.