1 code implementation • NAACL 2022 • Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang
We develop AcTune, a new framework that improves the label efficiency of active PLM fine-tuning by unleashing the power of unlabeled data via self-training.
1 code implementation • ACL 2022 • Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang
Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.
1 code implementation • 15 Sep 2022 • Yue Yu, Rongzhi Zhang, ran Xu, Jieyu Zhang, Jiaming Shen, Chao Zhang
We propose PATRON, a new method that uses prompt-based uncertainty estimation for data selection for pre-trained language model fine-tuning under cold-start scenarios, i. e., no initial labeled data are available.
no code implementations • 28 Jun 2022 • Rongzhi Zhang, Rebecca West, Xiquan Cui, Chao Zhang
We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions.
1 code implementation • 18 Mar 2022 • Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang
Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.
1 code implementation • 16 Dec 2021 • Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang
We propose {\ours}, a new framework that leverages unlabeled data to improve the label efficiency of active PLM fine-tuning.
no code implementations • 16 Dec 2021 • Rongzhi Zhang, Yulong Gu, Xiaoyu Shen, Hui Su
We introduce time interval embedding to represent the time pattern between the item that needs to be predicted and historical click, and use it to replace the position embedding in the original transformer (called temporal transformer).
1 code implementation • EMNLP 2020 • Rongzhi Zhang, Yue Yu, Chao Zhang
Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration.
1 code implementation • ACL 2019 • Hui Su, Xiaoyu Shen, Rongzhi Zhang, Fei Sun, Pengwei Hu, Cheng Niu, Jie zhou
To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network.