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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). Currently, I am working with prof. Hwee Tou Ng and prof. Qizhe Xie. I am supported by AI.SG 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

Publications

(* indicates equal contribution)

  • Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
      Hai Ye, Qizhe Xie, Hwee Tou Ng
      To appear at the Association for Computational Linguistic (ACL), 2023, Long 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 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings), 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]

  • Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model
      Juntao Li, Ruidan He, Hai Ye, Hwee Tou Ng, Lidong Bing, Rui Yan
       In Proceedings of International Joint Conferences on Artificial Intelligence (IJCAI), 2020.

  • 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]

  • Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction
      Hai Ye, Zhunchen Luo
      In Information Processing & Management (IPM), 2019
      [paper]

  • 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]

  • Interpretable Rationale Augmented Charge Prediction System
      Xin Jiang*, Hai Ye*, Zhunchen Luo, Wenhan Chao, Wenjia Ma
     In Proceedings of the International Conference on Computational Linguistics: System Demonstrations (COLING), 2018.

  • 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]

  • Dependency-tree Based Convolutional Stacked Neural Networks for Aspect Term Extraction
     Hai Ye, Zichao Yan, Zhunchen Luo, Wenhan Chao
     In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2017.
     [paper]

    Services

  • Reviewer: ARR Reviewer, EMNLP'22, EMNLP'21, ACL'21, *SEM'20, AACL-IJCNLP'20, EMNLP'20, ACL'20, SEM'19

  • Volunteer: NAACL 2018

    • Misc.

  • My favorite hobby: Music.

    • Last update: 2022