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CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of Pre-trained Language Models [article]

Yusheng Su, Xu Han, Yankai Lin, Zhengyan Zhang, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
2021 arXiv   pre-print
To address this issue, we introduce a novel framework (named "CSS-LM") to improve the fine-tuning phase of PLMs via contrastive semi-supervised learning.  ...  Fine-tuning pre-trained language models (PLMs) has demonstrated its effectiveness on various downstream NLP tasks recently.  ...  CONCLUSION AND FUTURE WORK In this work, we introduce the CSS-LM framework to improve the fine-tuning phase of PLMs via contrastive semi-supervised learning.  ... 
arXiv:2102.03752v3 fatcat:bv6kzaqcwfh7rcdxb4at4qsdi4

Table of Contents

2021 IEEE/ACM Transactions on Audio Speech and Language Processing  
Ji CSS-LM: A Contrastive Framework for Semi-Supervised Fine-Tuning of Pre-Trained Language Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Scharenborg Addressing Extraction and Generation Separately: Keyphrase Prediction With Pre-Trained Language Models . . . . . . ....Wang Adaptive Convolution for Semantic Role Labeling . . . . . . . . .  ... 
doi:10.1109/taslp.2021.3137066 fatcat:ocit27xwlbagtjdyc652yws4xa

Table of Contents

2021 IEEE/ACM Transactions on Audio Speech and Language Processing  
Tu CSS-LM: A Contrastive Framework for Semi-Supervised Fine-Tuning of Pre-Trained Language Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Shi Audio-Aware Spoken Multiple-Choice Question Answering With Pre-Trained Language Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/taslp.2021.3137064 fatcat:rpka3f2bhjh37c7pkhiowyndhm