1 code implementation • ACL 2022 • Hai Ye, Hwee Tou Ng, Wenjuan Han
In conversational question answering (CQA), the task of question rewriting~(QR) in context aims to rewrite a context-dependent question into an equivalent self-contained question that gives the same answer.
1 code implementation • 11 Jun 2023 • Hai Ye, Qizhe Xie, Hwee Tou Ng
In this work, we study multi-source test-time model adaptation from user feedback, where K distinct models are established for adaptation.
no code implementations • 25 Apr 2023 • Yi Su, Yixin Ji, Juntao Li, Hai Ye, Min Zhang
Accordingly, in this paper, we propose perturbation consistency learning (PCL), a simple test-time adaptation method to promote the model to make stable predictions for samples with distribution shifts.
1 code implementation • 9 Feb 2023 • Hai Ye, Yuyang Ding, Juntao Li, Hwee Tou Ng
To answer this question, we evaluate test-time adaptation (TTA) to improve a model after deployment.
no code implementations • ACL 2021 • Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jia-Wei Low, Lidong Bing, Luo Si
It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.
1 code implementation • 23 Nov 2020 • Juntao Li, Ruidan He, Hai Ye, Hwee Tou Ng, Lidong Bing, Rui Yan
Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.
2 code implementations • EMNLP 2020 • Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing
To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered.
no code implementations • 25 Jul 2019 • Hai Ye, Zhunchen Luo
Furthermore, to deal with the problem of class imbalance in distant supervision relation extraction, we further adopt cost-sensitive learning to rescale the costs from the positive and negative labels.
no code implementations • ACL 2019 • Hai Ye, Wenjie Li, Lu Wang
Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL description for some given MRs.
no code implementations • EMNLP 2018 • Hai Ye, Lu Wang
We study the problem of generating keyphrases that summarize the key points for a given document.
no code implementations • COLING 2018 • Xin Jiang, Hai Ye, Zhunchen Luo, WenHan Chao, Wenjia Ma
This paper proposes a neural based system to solve the essential interpretability problem existing in text classification, especially in charge prediction task.
1 code implementation • NAACL 2018 • Hai Ye, Xin Jiang, Zhunchen Luo, WenHan Chao
In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case.
1 code implementation • ACL 2017 • Hai Ye, WenHan Chao, Zhunchen Luo, Zhoujun Li
Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction.