Peng Qi

齐鹏

(pinyin: /qí péng/; ipa: /tɕʰǐ pʰə̌ŋ/)

I am a researcher at JD AI Research working on natural language processing and machine learning. If you are interested in exploring research opportunities with us (internship or full-time), don’t hesitate to reach out!

Before joining JD, I obtained my Ph.D. in Computer Science at Stanford University advised by Prof. Chris Manning, where I was a member of the natural language processing group.

My research goal is to build explainable machine learning systems to help us solve problems efficiently using textual knowledge. I believe that AI systems should be able to explain their computational decisions in a human-understandable manner, so as to build trust in their application to real-world problems. To this end, I have recently been working on natural language processing (NLP) techniques that help us answer complex questions from textual knowledge through explainable multi-step reasoning, as well as models that reason pragmatically about the knowledge of their interlocutors for efficient communication in dialogues, among others.

Outside of NLP research, I am broadly interested in presenting data in a more understandable manner, making technology appear less boring (to students, for example), and processing data with more efficient computation. I have also worked on speech recognition and computer vision previously.

When I procrastinate in my research life, I write code for Stanza, a natural language processing toolkit that’s available for a few dozen (human) languages, written in Python.

[CV (slightly outdated)]

education & professional experience

2020.10 -
Research Scientist, JD AI Research Silicon Valley Lab
2015.9 - 2020.9
Ph.D. & Research Assistant, Computer Science Department, Stanford University
2016.4 - 2017.3
Master of Science, Department of Statistics, Stanford University
2013.9 - 2015.6
Master of Science & Research Assistant, Computer Science Department, Stanford University
2012.7 - 2013.6
Research Assistant, State Key Laboratory of Intelligent Technology & Systems, Department of Computer Science and Technology, Tsinghua University
2008.8 - 2012.7
Bachelor of Engineering, School of Software, Tsinghua University (Excellent Graduate)


selected publications

(*=equal contribution)

  1. RepL4NLP
    Entity and Evidence Guided Document-Level Relation Extraction
    Kevin Huang, Peng Qi, Guangtao Wang, Tengyu Ma, and Jing Huang
    In 6th Workshop on Representation Learning for NLP (RepL4NLP) at ACL 2021, 2021.
    BibTeX
  2. TextGraphs
    Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification
    Xiaochen Hou, Jing Huang, Guangtao Wang, Peng Qi, Xiaodong He, and Bowen Zhou
    In TextGraphs-15 at NAACL 2021, 2021.
    BibTeX PDF
  3. arXiv
    Conversational AI Systems for Social Good: Opportunities and Challenges
    Peng Qi, Jing Huang, Youzheng Wu, Xiaodong He, and Bowen Zhou
    arXiv preprint arXiv:2105.06457, 2021.
    BibTeX PDF
  4. JAMIA
    Biomedical and Clinical English Model Packages for the Stanza Python NLP Library
    Yuhao Zhang, Yuhui Zhang, Peng Qi, Christopher D. Manning, and Curtis P. Langlotz
    Journal of the American Medical Informatics Association, 2021.
    BibTeX PDF
  5. NAACL
    Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
    Xiaochen Hou, Peng Qi, Guangtao Wang, Rex Ying, Jing Huang, Xiaodong He, and Bowen Zhou
    In 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021.
    BibTeX PDF
  6. EACL
    Do Syntax Trees Help Pre-trained Transformers Extract Information?
    Devendra Singh Sachan, Yuhao Zhang, Peng Qi, and William Hamilton
    In The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021.
    BibTeX PDF Code
  7. arXiv
    Retrieve, Read, Rerank, then Iterate: Answering Open-Domain Questions of Varying Reasoning Steps from Text
    Peng Qi*, Haejun Lee*, Oghenetegiri "TG" Sido*, and Christopher D. Manning
    arXiv preprint arXiv:2010.12527, 2020.
    BibTeX PDF
  8. PhD thesis
    Explainable and Efficient Knowledge Acquisition from Text
    Peng Qi
    Stanford University, 2020.
    BibTeX HTML PDF
  9. ACL (Demo)
    Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
    Peng Qi*, Yuhao Zhang*, Yuhui Zhang, Jason Bolton, and Christopher D. Manning
    In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2020.
    BibTeX PDF Code Website Presentation
  10. Findings
    Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations
    Peng Qi, Yuhao Zhang, and Christopher D. Manning
    In Findings of the Association for Computational Linguistics: EMNLP 2020, 2020.
    BibTeX PDF Code Presentation
  11. AlexaPrize
    Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
    Ashwin Paranjape*, Abigail See*, Kathleen Kenealy, Haojun Li, Amelia Hardy, Peng Qi, Kaushik Ram Sadagopan, Nguyet Minh Phu, Dilara Soylu, and Christopher D. Manning
    In 3rd Proceedings of Alexa Prize (Alexa Prize 2019), 2020.
    BibTeX PDF
  12. EMNLP-IJCNLP
    Answering Complex Open-domain Questions Through Iterative Query Generation
    Peng Qi, Xiaowen Lin*, Leo Mehr*, Zijian Wang*, and Christopher D. Manning
    In 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019.
    BibTeX PDF Blog Code Poster
  13. ACL
    Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context
    Urvashi Khandelwal, He He, Peng Qi, and Dan Jurafsky
    Association for Computational Linguistics (ACL), 2018.
    BibTeX PDF Code
  14. EMNLP
    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
    Zhilin Yang*, Peng Qi*, Saizheng Zhang*, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
    BibTeX PDF Blog
  15. EMNLP
    Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
    Yuhao Zhang*, Peng Qi*, and Christopher D. Manning
    In Empirical Methods in Natural Language Processing (EMNLP), 2018.
    BibTeX PDF Code Poster