Ask No More: Deciding when to guess in referential visual dialogue

Abstract

Our goal is to explore how the abilities brought in by a dialogue manager can be included in end-to-end visually grounded conversational agents. We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. Our analyses show that adding a decision making component produces dialogues that are less repetitive and that include fewer unnecessary questions, thus potentially leading to more efficient and less unnatural interactions.

arXiv commentCOLING 2018 (accepted)

References in corpus (6)

• Sequence to Sequence Learning with Neural Networks
• A Neural Conversational Model
• Deep Reinforcement Learning for Dialogue Generation
• Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
• Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
• Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning