November 2018

A Multimodal Dialogue System for Conversational Image Editing

Authors:

T.-h. Lin, T. Bui, D. S. Kim, and J. Oh

Workshop Paper

Abstract:

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 90% success rate under high error rates. We also conducted a real user study and analyzed real user behavior.

@workshop{Lin-2018-113122,
author = {T.-h. Lin And T. Bui And D. S. Kim And J. Oh},
title = {A Multimodal Dialogue System for Conversational Image Editing},
booktitle = {Proceedings of NeurIPS '18 2nd Workshop on Conversational AI},
year = {2018},
month = {November},
}