activity
20182022
most citedSemantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention

22 citations · 41 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CV202212 cited

Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models

Xichen Pan, Pengda Qin, Yuhong Li +2

Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-worl…

cs.CL2021

InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping

Runshi Liu, Pengda Qin, Yuhong Li +4

E-commerce companies have to face abnormal sellers who sell potentially-risky products. Typically, the risk can be identified by jointly considering product content (e.g., title an…

cs.CL20211 cited

TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data

Pengda Qin, Yuhong Li, Kefeng Deng +1

Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly. In a visual understanding application…

cs.CL20206 cited

Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

Pengda Qin, Xin Wang, Wenhu Chen +3

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion…

cs.CL2019

Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization

Siyao Li, Deren Lei, Pengda Qin +1

Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. Howe…

cs.CL2019

Multi-Task Self-Supervised Learning for Disfluency Detection

Shaolei Wang, Wanxiang Che, Qi Liu +3

Most existing approaches to disfluency detection heavily rely on human-annotated data, which is expensive to obtain in practice. To tackle the training data bottleneck, we investig…