no code implementations • 13 Jun 2021 • Runshi Liu, Pengda Qin, Yuhong Li, Weigao Wen, Dong Li, Kefeng Deng, Qiang Wu
Typically, the risk can be identified by jointly considering product content (e. g., title and image) and seller behavior.
no code implementations • 3 Jun 2021 • Pengda Qin, Yuhong Li, Kefeng Deng, Qiang Wu
Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly.
1 code implementation • 8 Jan 2020 • Pengda Qin, Xin Wang, Wenhu Chen, Chunyun Zhang, Weiran Xu, William Yang Wang
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.
no code implementations • IJCNLP 2019 • Siyao Li, Deren Lei, Pengda Qin, William Yang Wang
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.
no code implementations • 15 Aug 2019 • Shaolei Wang, Wanxiang Che, Qi Liu, Pengda Qin, Ting Liu, William Yang Wang
The pre-trained network is then fine-tuned using human-annotated disfluency detection training data.
2 code implementations • ACL 2019 • Wenhu Chen, Jianshu Chen, Pengda Qin, Xifeng Yan, William Yang Wang
Semantically controlled neural response generation on limited-domain has achieved great performance.
Ranked #5 on
Data-to-Text Generation
on MULTIWOZ 2.1
no code implementations • ACL 2018 • Pengda Qin, Weiran Xu, William Yang Wang
Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem.
2 code implementations • ACL 2018 • Pengda Qin, Weiran Xu, William Yang Wang
The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.