Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
By Sang-Woo Lee, Tong Gao, Sohee Yang, Jaejun Yoo, Jung-Woo Ha. 2019
18 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Multimodal Learning, Question Answering & Reading Comprehension
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Dhruv Batra
- Devi Parikh
- Trevor Darrell
- Luc Van Gool
- Dacheng Tao
- Abhishek Das
- Kristen Grauman
- Chunhua Shen
In Multimodal Learning
- Devi Parikh
- Dhruv Batra
- Abhishek Das
- Vishvak Murahari
- Mohit Bansal
- Zhe Gan
- Hanwang Zhang
- Sang-Min Park
In Question Answering & Reading Comprehension
- Devi Parikh
- Dhruv Batra
- Nazneen Fatema Rajani and Raymond J. Mooney
- Christopher D. Manning
- Drew A. Hudson
- Song-Chun Zhu
- Quanshi Zhang
- Raymond J. Mooney
Citations in the corpus
- Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline (2019, background): “Follow-up works on visual dialog have explored the use of deep reinforcement learning [14, 15, 28], transferring knowledge from discriminative to generative decoders [16], conditional variational autoencoders [17], generative adversarial networks [18], attention mechanisms for visual coreference res”
- A Metaverse: Taxonomy, Components, Applications, and Open Challenges (2022, background): “AQM (Questioner’s Mind) agents ask more consistent questions to maximize information acquisition in taskoriented conversational systems [159].”