Peng Qi

齐鹏

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

I recently graduated from Stanford! I have joined JD AI as a research scientist to continue working on NLP/ML-related research. If you are interested in exploring research opportunities with us (internship or full-time), don’t hesitate to reach out!

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 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.

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[CV (slightly outdated)]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. 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
  2. arXiv
    Retrieve, Rerank, Read, then Iterate: Answering Open-Domain Questions of Arbitrary Complexity from Text
    Peng Qi*, Haejun Lee*, Oghenetegiri "TG" Sido*, and Christopher D. Manning
    arXiv preprint arXiv:2010.12527, 2020.
    BibTeX PDF
  3. PhD thesis
    Explainable and Efficient Knowledge Acquisition from Text
    Peng Qi
    Stanford University, 2020.
    BibTeX HTML PDF
  4. 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
  5. Findings
    Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations
    Peng Qi, Yuhao Zhang, and Christopher D. Manning
    Findings of ACL: EMNLP 2020, 2020.
    BibTeX PDF Code Presentation
  6. arXiv
    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
    arXiv preprint arXiv:2008.12348, 2020.
    BibTeX PDF
  7. 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
  8. 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
  9. 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
  10. 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
  11. ACL
    Arc-swift: A Novel Transition System for Dependency Parsing
    Peng Qi, and Christopher D. Manning
    In Association for Computational Linguistics (ACL), 2017.
    BibTeX PDF Code Slides