Interactive Reinforcement Learning for Object Grounding via Self-Talking

Yan Zhu; Shaoting Zhang; Dimitris Metaxas

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arXiv · 1712.00576

Interactive Reinforcement Learning for Object Grounding via Self-Talking

Yan Zhu·Shaoting Zhang·Dimitris Metaxas

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Abstract

Humans are able to identify a referred visual object in a complex scene via a few rounds of natural language communications. Success communication requires both parties to engage and learn to adapt for each other. In this paper, we introduce an interactive training method to improve the natural language conversation system for a visual grounding task. During interactive training, both agents are reinforced by the guidance from a common reward function. The parametrized reward function also cooperatively updates itself via interactions, and contribute to accomplishing the task. We evaluate the method on GuessWhat?! visual grounding task, and significantly improve the task success rate. However, we observe language drifting problem during training and propose to use reward engineering to improve the interpretability for the generated conversations. Our result also indicates evaluating goal-ended visual conversation tasks require semantic relevant metrics beyond task success rate.

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Yan Zhu, Shaoting Zhang, Dimitris Metaxas. 2017-12-02. Interactive Reinforcement Learning for Object Grounding via Self-Talking. https://arxiv.org/abs/1712.00576

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