# Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts

arXiv: https://arxiv.org/abs/1907.10500 · HTML: https://mlsanity.com/paper/1907.10500/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review](https://mlsanity.com/paper/2207.00782/) (2022)

## Abstract (arXiv)

This paper tackles the problem of learning a questioner in the goal-oriented visual dialog task. Several previous works adopt model-free reinforcement learning. Most pretrain the model from a finite set of human-generated data. We argue that using limited demonstrations to kick-start the questioner is insufficient due to the large policy search space. Inspired by a recently proposed information theoretic approach, we develop two analytic experts to serve as a source of high-quality demonstrations for imitation learning. We then take advantage of reinforcement learning to refine the model towards the goal-oriented objective. Experimental results on the GuessWhat?! dataset show that our method has the combined merits of imitation and reinforcement learning, achieving the state-of-the-art performance.