# What Should I Ask? Using Conversationally Informative Rewards for Goal-Oriented Visual Dialog

arXiv: https://arxiv.org/abs/1907.12021 · HTML: https://mlsanity.com/paper/1907.12021/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2019.

## Built on by (most cited first, 14 of 14)

- [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://mlsanity.com/paper/2102.01672/) (2021)
- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation](https://mlsanity.com/paper/2105.11541/) (2021)
- [Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue](https://mlsanity.com/paper/2106.15550/) (2021)
- [Closed-book Question Generation via Contrastive Learning](https://mlsanity.com/paper/2210.06781/) (2022)
- [Saying the Unseen: Video Descriptions via Dialog Agents](https://mlsanity.com/paper/2106.14069/) (2021)
- [Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser](https://mlsanity.com/paper/2109.02297/) (2021)
- [Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy](https://mlsanity.com/paper/2109.05312/) (2021)
- [What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation](https://mlsanity.com/paper/2109.07129/) (2021)
- [The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues](https://mlsanity.com/paper/2103.11151/) (2021)
- [Modeling Explicit Concerning States for Reinforcement Learning in Visual Dialogue](https://mlsanity.com/paper/2107.05250/) (2021)
- [Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review](https://mlsanity.com/paper/2207.00782/) (2022)
- [Spot the Difference: A Cooperative Object-Referring Game in Non-Perfectly Co-Observable Scene](https://mlsanity.com/paper/2203.08362/) (2022)
- [Dialogue Telemetry: Turn-Level Instrumentation for Autonomous Information Gathering](https://mlsanity.com/paper/2601.09570/) (2026)

## Abstract (arXiv)

The ability to engage in goal-oriented conversations has allowed humans to gain knowledge, reduce uncertainty, and perform tasks more efficiently. Artificial agents, however, are still far behind humans in having goal-driven conversations. In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective. This task is challenging since these questions must not only be consistent with a strategy to achieve a goal, but also consider the contextual information in the image. We propose an end-to-end goal-oriented visual dialogue system, that combines reinforcement learning with regularized information gain. Unlike previous approaches that have been proposed for the task, our work is motivated by the Rational Speech Act framework, which models the process of human inquiry to reach a goal. We test the two versions of our model on the GuessWhat?! dataset, obtaining significant results that outperform the current state-of-the-art models in the task of generating questions to find an undisclosed object in an image.