Learning Personalized End-to-End Goal-Oriented Dialog
This work addresses the issue of impersonal dialog systems for users in goal-oriented conversations, representing an incremental advance by integrating personalization into existing frameworks.
The paper tackles the problem of goal-oriented dialog systems lacking personalization by introducing a model that encodes user profiles and preferences, achieving qualitative performance improvements and higher task completion rates and user satisfaction in human evaluations.
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. The two models are combined into the Personalized MemN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.