# Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation

arXiv: https://arxiv.org/abs/1902.08355 · HTML: https://mlsanity.com/paper/1902.08355/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues](https://mlsanity.com/paper/2103.11151/) (2021)
- [Spot the Difference: A Cooperative Object-Referring Game in Non-Perfectly Co-Observable Scene](https://mlsanity.com/paper/2203.08362/) (2022)
- [Multi-Modal Dialogue State Tracking for Playing GuessWhich Game](https://mlsanity.com/paper/2408.08431/) (2024)

## Abstract (arXiv)

Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses.