MoCoUTRL: a momentum contrastive framework for unsupervised text representation learning

This paper presents MoCoUTRL: a Momentum Contrastive Framework for Unsupervised Text Representation Learning. This model improves two aspects of recently popular contrastive learning algorithms in natural language processing (NLP). Firstly, MoCoUTRL employs multi-granularity semantic contrastive lea...

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書誌詳細

主要な著者:	Ao Zou, Wenning Hao, Dawei Jin, Gang Chen, Feiyan Sun
フォーマット:	Artigo
言語:	Inglês
出版事項:	Taylor & Francis Group 2023-12-01
シリーズ:	Connection Science
主題:	natural language processing text representation learning momentum contrast alignment uniformity
オンライン･アクセス:	http://dx.doi.org/10.1080/09540091.2023.2221406
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