Publications

2022

• Nearest Neighbor Zero-Shot Inference
  Weijia Shi, Julian Michael, Suchin Gururangan, Luke Zettlemoyer
  Preprint [paper]

2021

• DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions
  Weijia Shi, Mandar Joshi, Luke Zettlemoyer
  In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021. [paper]

• Cross-lingual entity alignment for knowledge graphs with incidental supervision from free text
  Muhao Chen*, Weijia Shi*, Ben Zhou, Dan Roth
  In the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021. [paper]

2020

• Design Challenges for Low-resource Cross-lingual Entity Linking
  Xingyu Fu*, Weijia Shi*, Zian Zhao, Xiaodong Yu, Dan Roth
  In Proceedings of the 25th Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. [paper]

• On Tractable Representations of Binary Neural Networks
  Weijia Shi, Andy Shih, Adnan Darwiche, Arthur Choi
  In Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR) 2020. [paper]

2019

• Retrofitting contextualized word embeddings with paraphrases
  Weijia Shi*, Muhao Chen*, Pei Zhou, Kai-Wei Chang
  In Proceedings of the 25th Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019. [paper]

• Examining gender bias in languages with grammatical gender
  Pei Zhou, Weijia Shi, Jieyu Zhao, Kuan-Hao Huang, Muhao Chen, Ryan Cotterell, Kai-Wei Chang
  In Proceedings of the 25th Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019. [paper]

• Embedding uncertain knowledge graphs
  Xuelu Chen, Muhao Chen, Weijia Shi, Yizhou Sun, Carlo Zaniolo
  In Proceedings of the 25th Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019. [paper]

• Learning bilingual word embeddings using lexical definitions
  Weijia Shi, Muhao Chen, Yingtao Tian, Kai-Wei Chang
  In Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019). [paper]

• Compiling neural networks into tractable Boolean circuits
  Arthur Choi, Weijia Shi, Andy Shih, Adnan Darwiche
  In AAAI Spring Symposium on Verification of Neural Networks (VNN) [paper]