A COMPARATIVE STUDY ON REINFORCEMENT LEARNING BASED VISUAL DIALOG SYSTEMS

Abstract

: Recently the conjunction between vision and language has created many intersecting tasks as visual question-answering systems, image captioning, etc. Specifically, dialog systems that depend on a visual scene play an important role in improving human-computer interaction technology. At the same time, reinforcement learning has emerged as a very successful paradigm for a variety of machine learning tasks, especially those tasks that aim to develop smart and humanoid machines. In this paper, we show how reinforcement learning is applied to conversational agents to build a powerful visual dialog agent. Visual Dialog task requires the agent to have a meaningful conversation about visual content in natural language. For a given image, its caption, dialog history (question/answer pairs)