publications

(*=equal contribution)

2020

  1. 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.
  2. PhD thesis
    Explainable and Efficient Knowledge Acquisition from Text
    Peng Qi
    Stanford University, 2020.
  3. 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.
  4. 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.
  5. 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.
  6. arXiv
    Do Syntax Trees Help Pre-trained Transformers Extract Information?
    Devendra Singh Sachan, Yuhao Zhang, Peng Qi, and William Hamilton
    arXiv preprint arXiv:2008.09084, 2020.

2019

  1. 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.

2018

  1. 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.
  2. CoNLL
    Universal Dependency Parsing from Scratch
    Peng Qi*, Timothy Dozat*, Yuhao Zhang*, and Christopher D. Manning
    In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, 2018.
  3. 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.
  4. 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.

2017

  1. CoNLL
    Stanford’s Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task
    Timothy Dozat, Peng Qi, and Christopher D. Manning
    In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, 2017. 1st place
  2. TAC
    Stanford at TAC KBP 2017: Building a Trilingual Relational Knowledge Graph.
    Arun Tejasvi Chaganty, Ashwin Paranjape, Jason Bolton, Matthew Lamm, Jinhao Lei, Abigail See, Kevin Clark, Yuhao Zhang, Peng Qi, and Christopher D Manning
    In Text Analysis Conference (TAC) Proceedings, 2017.
  3. ACL
    Arc-swift: A Novel Transition System for Dependency Parsing
    Peng Qi, and Christopher D. Manning
    In Association for Computational Linguistics (ACL), 2017.

2016

  1. CS&L
    Building DNN Acoustic Models for Large Vocabulary Speech Recognition
    Andrew L Maas, Peng Qi, Ziang Xie, Awni Y Hannun, Christopher T Lengerich, Daniel Jurafsky, and Andrew Y Ng
    Computer Speech & Language, 2016.
  2. TAC
    Stanford at TAC KBP 2016: Sealing Pipeline Leaks and Understanding Chinese
    Yuhao Zhang, Arun Chaganty, Ashwin Paranjape, Danqi Chen, Jason Bolton, Peng Qi, and Christopher D Manning
    In Text Analysis Conference (TAC), 2016.

2014

  1. Neurocomputing
    Modeling response properties of V2 neurons using a hierarchical K-means model
    Xiaolin Hu, Jianwei Zhang, Peng Qi, and Bo Zhang
    Neurocomputing, 2014.
  2. Neural Comp.
    Learning nonlinear statistical regularities in natural images by modeling the outer product of image intensities
    Peng Qi, and Xiaolin Hu
    Neural computation, 2014.

2013

  1. ICACI
    Modeling Outer Products of Features for Image Classification
    Peng Qi, Shuochen Su, and Xiaolin Hu
    In Advanced Computational Intelligence (ICACI), 2013 Sixth International Conference on, 2013.

2012

  1. ICONIP
    Hierarchical K-Means Algorithm for Modeling Visual Area V2 Neurons
    Xiaolin Hu, Peng Qi, and Bo Zhang
    2012. Best Paper Award