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

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

7 February 2021

Xu Han

Yankai Lin

Zhengyan Zhang

Zhiyuan Liu

Peng Li

Maosong Sun

Papers citing "CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of Pre-trained Language Models"

5 / 5 papers shown

Title
Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition Weidong Chen Xiaofen Xing Peihao Chen Xiangmin Xu VLM 30 35 0 20 Jul 2023
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives Nils Rethmeier Isabelle Augenstein SSL VLM 90 90 0 25 Feb 2021
A Mutual Information Maximization Perspective of Language Representation Learning Lingpeng Kong Cyprien de Masson dÁutume Wang Ling Lei Yu Zihang Dai Dani Yogatama SSL 226 165 0 18 Oct 2019
Knowledge Enhanced Contextual Word Representations Matthew E. Peters Mark Neumann IV RobertL.Logan Roy Schwartz Vidur Joshi Sameer Singh Noah A. Smith 234 656 0 09 Sep 2019
Text Summarization with Pretrained Encoders Yang Liu Mirella Lapata MILM 258 1,433 0 22 Aug 2019