A Multimodal Dialogue System for Conversational Image Editing

Proc. of 2nd Conversational AI Workshop - NIPS

Publication date: December 13, 2018

Tzu-Hsiang Lin, Trung Bui, Doo Soon Kim, Jean Oh

In this paper, we present a multimodal dialogue system for Conversational Image Editing. We formulate our multimodal dialogue system as a Partially Observed Markov Decision Process (POMDP) and trained it with Deep Q-Network (DQN) and a user simulator. Our evaluation shows that the DQN policy outperforms a rule-based baseline policy, achieving a 90% success rate under high error rates. We also conducted a real user study and analyzed real user behavior.

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