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Bio:

Hi! I am Hai Ye (叶海). I am a Ph.D. candidate at the School of Computing, National University of Singapore (NUS), where I am working with prof. Hwee Tou Ng. I am supported by AISG Ph.D. Fellowship. I obtained my bachelor degree from Beihang University, Beijing, China.

Research:

My research goal is to create a robust NLP model that can understand natural language at a human level and interact with users. To achieve this, I focus on improving the model's ability to handle unexpected situations and involve humans in the loop for reliable decision-making and continuous improvement.

[A Collection of Language Model Alignment and Beyond]

Contact: hye.me [at] outlook [dot] com

Selected Publications [Full]

(* indicates equal contribution)

  • Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
      Hai Ye, Qizhe Xie, Hwee Tou Ng
      In Proceedings of the Association for Computational Linguistic (ACL), 2023, Long paper.
      [paper]

  • Robust Question Answering against Distribution Shifts with Test-Time Adaptation: An Empirical Study
      Hai Ye, Yuyang Ding, Juntao Li, Hwee Tou Ng
      In Findings of the Association for Computational Linguistics: EMNLP 2022 , Long paper.
      [paper] [code]

  • On the Robustness of Question Rewriting Systems to Questions of Varying Hardness
      Hai Ye, Hwee Tou Ng, Wenjuan Han
      In Proceedings of the Association for Computational Linguistic (ACL), 2022, Long paper.
      [paper]

  • On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
      Ruidan He*, Linlin Liu*, Hai Ye*, Qingyu Tan, Bosheng Ding, Liying Cheng, David Low, Lidong Bing, Luo Si
      In Proceedings of the Association for Computational Linguistic (ACL), 2021, Long paper.
      [paper]

  • Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training
      Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing
      In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020, Long paper.
      [paper] [code]

  • Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization
      Hai Ye, Wenjie Li, Lu Wang
      In Proceedings of the Association for Computational Linguistic (ACL), 2019, Long paper.
      [paper] [poster]

  • Semi-Supervised Learning for Neural Keyphrase Generation
      Hai Ye, Lu Wang
      In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018, Long paper.
      [paper]

  • Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
      Hai Ye*, Xin Jiang*, Zhunchen Luo*, Wenhan Chao
      In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics
     (NAACL), 2018, Long paper, Oral.
      [paper] [Data] [Slides]

  • Jointly Extracting Relations with Class Ties via Effective Deep Ranking
     Hai Ye, Wenhan Chao, Zhunchen Luo, Zhoujun Li
     In Proceedings of the Association for Computational Linguistic (ACL), 2017, Long paper.
     [paper] [code]

    Award

  • AISG PhD Fellowship.

    • Academic Services

  • Reviewer: ARR, ACL, NAACL, EMNLP, COLING

  • Volunteer: NAACL 2018

    • Last update: 2023