Learning Personalized End-to-End Goal-Oriented Dialog

Understand

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues.

• In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs.
• We first introduce a Profile Model which encodes user profiles into distributed embeddings and refers to conversation history from other similar users.
• Then a Preference Model captures user preferences over knowledge base entities to handle the ambiguity in user requests.