Download PDFOpen PDF in browserSemi-Supervised Learning in NLP: Leveraging Large-Scale Unlabeled Data for Model TrainingEasyChair Preprint 122708 pages•Date: February 24, 2024AbstractThis paper explores the efficacy of leveraging large-scale unlabeled data for model training in NLP tasks. This paper explores various techniques and methodologies employed in semi-supervised learning for NLP, focusing on how large-scale unlabeled data can be effectively utilized to enhance model training. The theoretical foundations of semi-supervised learning, including methods such as self-training, co-training, and multi-view learning, are discussed, highlighting their applications and effectiveness in NLP tasks. Additionally, recent advancements in neural network architectures, such as pre-training and fine-tuning strategies, which have significantly contributed to the success of semi-supervised learning in NLP, are reviewed. Furthermore, challenges and future directions in semi-supervised learning for NLP, including scalability, domain adaptation, and robustness to noisy data, are examined. Keyphrases: learning, semi, supervised
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