IEEE模糊系统会刊TFS(IEEE Transactions on Fuzzy Systems)是由IEEE所属神经网络联合会发起和创办的一种新出版物。刊登有关模糊系统的理论、设计和应用方面的高质量技术论文,特别重视工程系统和科学应用,同时刊登信息以及有关其所载文章的评论和反驳。 官网地址:http://dblp.uni-trier.de/db/journals/tfs/

最新论文

Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data. However, it's unclear whether they learn similar representations or they can be effectively combined. In this paper, we show that TAPT and ST can be complementary with simple TFS protocol by following TAPT -> Finetuning -> Self-training (TFS) process. Experimental results show that TFS protocol can effectively utilize unlabeled data to achieve strong combined gains consistently across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. We investigate various semi-supervised settings and consistently show that gains from TAPT and ST can be strongly additive by following TFS procedure. We hope that TFS could serve as an important semi-supervised baseline for future NLP studies.

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