290 citations · 1.9k across the 59 of their papers we have counts for
8 papers · 1 filter
Sequential Attention GAN for Interactive Image Editing
Yu Cheng, Zhe Gan, Yitong Li +2
Most existing text-to-image synthesis tasks are static single-turn generation, based on pre-defined textual descriptions of images. To explore more practical and interactive real-l…
StoryGAN: A Sequential Conditional GAN for Story Visualization
Yitong Li, Zhe Gan, Yelong Shen +6
We propose a new task, called Story Visualization. Given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence. In contrast…
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Yuexin Wu, Xiujun Li, Jingjing Liu +2
Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integratin…
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu +3
We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-o…
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
Shang-Yu Su, Xiujun Li, Jianfeng Gao +2
This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the D…
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
Yichong Xu, Xiaodong Liu, Yelong Shen +2
We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains. Inspire…
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems
Xiujun Li, Yu Wang, Siqi Sun +3
This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate…
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning
Baolin Peng, Xiujun Li, Jianfeng Gao +3
Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user s…