What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.
By Pushkar Shukla, Carlos E. L. Elmadjian, R. Sharan, Vivek Kulkarni, Matthew A. Turk, William Yang Wang. 2019
35 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning, Question Answering & Reading Comprehension, Dialogue & Conversational AI
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sebastian Gehrmann
- Percy Liang
- Thibault Sellam
- Mohit Bansal
- Yejin Choi
- Jianfeng Gao
- Mirella Lapata
- Elizabeth Clark
In Computer Vision and Pattern Recognition
- Sebastian Gehrmann
- Thibault Sellam
- Ankur P. Parikh
- Mihir Kale
- Hendrik Strobelt
- Esin Durmus
- Faisal Ladhak
- Anastasia Shimorina
In Multimodal Learning
- Tatsunori B. Hashimoto
- Yacine Jernite
- João Sedoc
- Sebastian Gehrmann
- Bodhisattwa Prasad Majumder
- Emiel van Miltenburg
In Question Answering & Reading Comprehension
In Dialogue & Conversational AI
Citations in the corpus
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021, positive): “Visual Question Generation (Shukla et al., 2019; Li et al., 2018) 28.”