Peng Li's photo

Peng Li

Research Associate Professor

Institute for AI Industry Research (AIR), Tsinghua University, China
Contact: pengli09 at gmail.com

Bio

Peng Li is a Research Associate Professor at Institute for AI Industry Research (AIR), Tsinghua University. He received his B.S. degree in computer science and technology from Tsinghua University in 2009 and his Ph.D. in computer science and technology from Tsinghua University in 2015. Before joining Tsinghua, he was a principal researcher and team leader at WeChat AI, Tencent. He also previously worked at Institute of Deep Learning, Baidu Inc. His main research interests include natural language processing, pre-trained models, question answering, information extraction, machine translation, and dialogue system. He was the recipient of First Prize of Qian Weichang Chinese Information Processing Science and Technology Award and Second Prize of Chinese Institute of Electronics Science and Technology Progress Award.

Publications

Google Scholar Citations

    2022

  1. Weize Chen, Xu Han, Yankai Lin, Hexu Zhao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou. Fully Hyperbolic Neural Networks. To appear in Proceedings of ACL 2022.
  2. Yikang Shen, Shawn Tan, Alessandro Sordoni, Peng Li, Jie Zhou, Aaron Courville. Unsupervised Dependency Graph Network. To appear in Proceedings of ACL 2022.
  3. Deming Ye, Yankai Lin, Peng Li, Maosong Sun. Packed Levitated Marker for Entity and Relation Extraction. To appear in Proceedings of ACL 2022.
  4. Pei Ke, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Xiaoyan Zhu, Minlie Huang. CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation. To appear in Proceedings of ACL 2022.
  5. Deming Ye, Yankai Lin, Peng Li, Maosong Sun, Zhiyuan Liu. A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models. To appear in Proceedings of ACL 2022 (short).
  6. Yujia Qin, Jiajie Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou. ELLE: Efficient Lifelong Pre-training for Emerging Data. To appear in Findings of ACL 2022.
  7. Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou. Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach. To appear in Findings of ACL 2022.
  8. Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou. MoEfication: Transformer Feed-forward Layers are Mixtures of Experts. To appear in Findings of ACL 2022.

    9. 2021

  9. Deli Chen, Yankai Lin, Guangxiang Zhao, Xuancheng Ren, Peng Li, Jie Zhou, Xu Sun. Topology-Imbalance Learning for Semi-Supervised Node Classification. In Proceedings of NeurIPS 2021. [pdf]
  10. Lei Li, Yankai Lin, Shuhuai Ren, Peng Li, Jie Zhou, and Xu Sun. Dynamic Knowledge Distillation for Pre-trained Language Models. In Proceedings of EMNLP 2021, 379-389. [pdf]
  11. Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models. In Proceedings of EMNLP 2021, 8365-8381. [pdf]
  12. Yuan Yao, Jiaju Du, Yankai Lin, Peng Li, Zhiyuan Liu, Jie Zhou, and Maosong Sun. CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild. In Proceedings of EMNLP 2021, 4452-4472. [pdf]
  13. Lei Li, Yankai Lin, Deli Chen, Shuhuai Ren, Peng Li, Jie Zhou and Xu Sun. CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade. In Findings of EMNLP 2021, 475-486. [pdf]
  14. Jifan Yu, Yuquan Wang, Qingyang Zhong, Gan Luo, Yiming Mao, Kai Sun, Wenzheng Feng, Wei Xu, Shulin Cao, Kaisheng Zeng, Zijun Yao, Lei Hou, Yankai Lin, Peng Li, Jie Zhou, Bin Xu, Juanzi Li, Jie Tang, and Maosong Sun. 2021. MOOCCubeX: A Large Knowledge-centered Repository for Adaptive Learning in MOOCs. In Proceedings of CIKM 2021, 4643-4652. [pdf]
  15. Yusheng Su, Xu Han, Zhengyan Zhang, Peng Li, Zhiyuan Liu, Yankai Lin, Jie Zhou, and Maosong Sun. 2021. CokeBERT: Contextual Knowledge Selection and Embedding towards Enhanced Pre-Trained Language Models. AI Open, 2021(2):127-134. [pdf] [code]
  16. Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, and Jie Zhou. 2021. ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning. In Proceedings of ACL-IJCNLP 2021, 3350-3363. [pdf]
  17. Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. 2021. Rethinking Stealthiness of Backdoor Attack against NLP Models. In Proceedings of ACL-IJCNLP 2021, 5543-5557. [pdf]
  18. Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, and Jie Zhou. 2021. CLEVE: Contrastive Pre-training for Event Extraction. In Proceedings of ACL-IJCNLP 2021, 6283-6297. [pdf]
  19. Feilong Chen, Xiuyi Chen, Fandong Meng, Peng Li, and Jie Zhou. 2021. GoG: Relation-aware Graph-over-Graph Network for Visual Dialog. Findings of ACL-IJCNLP 2021, 230-243. [pdf]
  20. Feilong Chen, Fandong Meng, Xiuyi Chen, Peng Li, and Jie Zhou. 2021. Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation. Findings of ACL-IJCNLP 2021, 436-446. [pdf]
  21. Xiuyi Chen, Feilong Chen, Fandong Meng, Peng Li, and Jie Zhou. 2021. Unsupervised Knowledge Selection for Dialogue Generation. Findings of ACL-IJCNLP 2021, 1230-1244. [pdf]
  22. Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2021. Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction. Findings of ACL-IJCNLP 2021, 1306-1318. [pdf]
  23. Huimin Chen, Yankai Lin, Fanchao Qi, Jinyi Hu, Peng Li, Jie Zhou, and Maosong Sun. 2021. Aspect-Level Sentiment-Controllable Review Generation with Mutual Learning Framework. In Proceedings of AAAI 2021, 12639-12647. [pdf]
  24. Qiu Ran, Yankai Lin, Peng Li, and Jie Zhou. 2021. Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information. In Proceedings of AAAI 2021, 13727-13735. [pdf]
  25. Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, and Jie Zhou. 2021. Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition. In Proceedings of NAACL 2021 (short), 1592–1599. [pdf]
  26. Yusheng Su, Xu Han, Yankai Lin, Zhengyan Zhang, Zhiyuan Liu, Peng Li, Jie Zhou, and Maosong Sun. 2020. CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of Pre-trained Language Models. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 2021(29):2930-2941. [pdf]
  27. Xianfeng Zeng, Yijin Liu, Ernan Li, Qiu Ran, Fandong Meng, Peng Li, Jinan Xu, and Jie Zhou. 2021. WeChat Neural Machine Translation Systems for WMT21. [pdf]
  28. Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, and Jie Zhou. 2021. MS-Ranker: Accumulating Evidence from Potentially Correct Candidates via Reinforcement Learning for Answer Selection. Neurocomputing, 2021(449):270-279. [pdf]
  29. Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2020. Knowledge Inheritance for Pre-trained Language Models. [arXiv]

    30. 2020

  30. Xiuyi Chen, Fandong Meng, Peng Li, Feilong Chen, Shuang Xu, Bo Xu, and Jie Zhou. 2020. Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation. In Proceedings of EMNLP 2020, 3426–3437. [pdf]
  31. Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, and Jie Zhou. 2020. Learning from Context or Names? An Empirical Study on Neural Relation Extraction. In Proceedings of EMNLP 2020, 3661–3672. [pdf] [code]
  32. Deming Ye, Yankai Lin, Jiaju Du, Zhenghao Liu, Peng Li, Maosong Sun, and Zhiyuan Liu. 2020. Coreferential Reasoning Learning for Language Representation. In Proceedings of EMNLP 2020, 7170–7186. [pdf] [code]
  33. Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie Zhou, and Yan Zhang. 2020. Disentangle-based Continual Graph Representation Learning. In Proceedings of EMNLP 2020, 2961-2972. [pdf] [code]
  34. Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, and Jie Zhou. 2020. MAVEN: A Massive General Domain Event Detection Dataset. In Proceedings of EMNLP 2020, 1652–1671. [pdf] [code]
  35. Fandong Meng, Jianhao Yan, Yijin Liu, Yuan Gao, Xianfeng Zeng, Qinsong Zeng, Peng Li, Ming Chen, Jie Zhou, Sifan Liu, and Hao Zhou. 2020. WeChat Neural Machine Translation Systems for WMT20. In Proceedings of WMT20, 239–247. [pdf]
  36. Qiu Ran*, Yankai Lin*, Peng Li*, and Jie Zhou. 2020. Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation. In Proceedings of ACL 2020, 3059–3069. [pdf] [code]
  37. Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2020. Continual Relation Learning via Episodic Memory Activation and Reconsolidation. In Proceedings of ACL 2020, 6429–6440. [pdf] [code]
  38. Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. 2020. Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View. In Proceedings of AAAI 2020, 3438-3445. [pdf]
  39. Feilong Chen, Fandong Meng, Jiaming Xu, Peng Li, Bo Xu, and Jie Zhou. 2020. DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog. In Proceedings of AAAI 2020, 7504-7511. [pdf] [code]
  40. Xiaozhi Wang, Shengyu Jia, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, and Jie Zhou. 2020. Neural Gibbs Sampling for Joint Event Argument Extraction. In Proceedings of AACL 2020, 169-180. [pdf]
  41. Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, and Maosong Sun. 2020. More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction. In Proceedings of AACL 2020, 745-758. [pdf]

    42. 2019

  42. Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, and Zhiyuan Liu. 2019. NumNet: Machine Reading Comprehension with Numerical Reasoning. In Proceedings of EMNLP 2019, 2474-2484. [pdf] [code]
  43. Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, and Xiang Ren. 2019. HMEAE: Hierarchical Modular Event Argument Extraction. In Proceedings of EMNLP 2019 (short), 5777–5783. [pdf] [code]
  44. Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2019. FewRel 2.0: Towards More Challenging Few-Shot Relation Classification. In Proceedings of EMNLP 2019 (short), 6250–6255. [pdf] [code] [benchmark]
  45. Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun. 2019. DocRED: A Large-Scale Document-Level Relation Extraction Dataset. In Proceedings of ACL 2019, 764-777. [pdf] [code & data] [CodaLab]
  46. Fuli Luo, Peng Li, Pengcheng Yang, Jie Zhou, Yutong Tan, Baobao Chang, Zhifang Sui, and Xu Sun. 2019. Towards Fine-grained Text Sentiment Transfer. In Proceedings of ACL 2019, 2013-2022. [pdf] [code]
  47. Shuming Ma, Pengcheng Yang, Tianyu Liu, Peng Li, Jie Zhou, and Xu Sun. 2019. Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation. In Proceedings of ACL 2019, 2047-2057. [pdf] [code]
  48. Fuli Luo, Peng Li, Jie Zhou, Pengcheng Yang, Baobao Chang, Xu Sun, and Zhifang Sui. 2019. A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer. In Proceedings of IJCAI 2019, 5116-5122. [pdf] [code]
  49. Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, and Peng Li. 2019. Adversarial Training for Weakly Supervised Event Detection. In Proceedings of NAACL 2019, 998-1008. [pdf] [code]
  50. Deli Chen, Xiaoqian Liu, Yankai Lin, Peng Li, Jie Zhou, Qi Su, and Xu Sun. 2019. HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Networks. arXiv.
  51. Qiu Ran, Peng Li, Weiwei Hu, and Jie Zhou. 2019. Option Comparison Network for Multiple-choice Reading Comprehension. [arXiv] [code]

    52. 2008~2018

  52. Xu Han, Pengfei Yu, Zhiyuan Liu, Maosong Sun, and Peng Li. 2018. Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention. In Proceedings of EMNLP 2018, 2236-2245. [pdf] [code]
  53. Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu. 2016. Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering. arXiv. [code (in the directory "neural_qa", veryfied on Ubuntu 16.04 with PaddlePaddle 0.10.5)]
  54. Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. 2016. Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation. Transactions of the Association for Computational Linguistics (TACL), 2016(4):371-383. [pdf]
  55. Peng Li, Yang Liu, Maosong Sun, Tatsuya Izuha, and Dakun Zhang. 2014. A Neural Reordering Model for Phrase-based Translation. In Proceedings of COLING 2014, 1897-1907. [pdf] [slides]
  56. Peng Li, Yang Liu, and Maosong Sun. 2013. Recursive Autoencoders for ITG-based Translation. In Proceedings of EMNLP 2013, 567-577. [pdf] [Talk at MSRA Ph.D Forum]
  57. Peng Li, Yang Liu, and Maosong Sun. 2013. An Extended GHKM Algorithm for Inducing Lambda-SCFG. In Proceedings of AAAI-13, 605-611. [pdf] [slides]
  58. Peng Li, Yang Liu, and Maosong Sun. 2012. A Beam Search Algorithm for ITG Word Alignment. In Proceedings of COLING 2012: Posters, 673-682. [pdf].
  59. Peng Li, Maosong Sun, and Ping Xue. 2010. Fast-Champollion: A Fast and Robust Sentence Alignment Algorithm. In Proceedings of COLING 2010: Posters, 710-718. [pdf]
  60. Xiance Si, Zhiyuan Liu, Peng Li, Qixia Jiang, and Maosong Sun. 2009. Content-based and Graph-based Tag Suggestion. In Proceedings of ECML/PKDD 2009 Discovery Challenge Workshop, 243-260. [pdf]
  61. Zhiyuan Liu, Peng Li, Yabin Zheng, and Maosong Sun. 2009. Clustering to Find Exemplar Terms for Keyphrase Extraction. In Proceedings of EMNLP 2009, 257-266. [pdf]
  62. Zhiyuan Liu, Peng Li, Yabin Zheng, Maosong Sun. Community Detection by Affinity Propagation. Technical Report, 2008. [pdf]

Software

  1. str2vec is a toolkit for computing vector-space representations for variable-length phrases using recursive autoencoders (RAE). In this document, we demostrate
  2. hiero-decoder-py is a hierarchical phrase-based translation decoder which supports parallel decoding. New feature functions can also be implemented easily.

Education

Locations of visitors to this page
Last-updated: Mar 19, 2022.