Search Results for author: Rongzhi Zhang

Found 9 papers, 7 papers with code

AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models

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.

Active Learning Pretrained Language Models +2

Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

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.

Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach

1 code implementation15 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.

Language Modelling Text Classification

Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction

no code implementations28 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.

Language Modelling Product Recommendation

PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

1 code implementation18 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.

Knowledge-enhanced Session-based Recommendation with Temporal Transformer

no code implementations16 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).

Graph Representation Learning Session-Based Recommendations

SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

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.

Active Learning Data Augmentation +3

Improving Multi-turn Dialogue Modelling with Utterance ReWriter

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.

Coreference Resolution Dialogue Rewriting

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