Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems

Understand

Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient.

• One common practice for this problem is to share training dialogues between different users and train multiple sequence-to-sequence dialogue models together with transfer learning.
• However, current sequence-to-sequence transfer learning models operate on the entire sentence, which might cause negative transfer if different personal information from different users is mixed up.
• We propose a personalized decoder model to transfer finer granularity phrase-level knowledge between different users while keeping personal preferences of each user intact.