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• W2970975962 abstract "In transductive learning, an unlabeled test set is used for model training. Although this setting deviates from the common assumption of a completely unseen test set, it is applicable in many real-world scenarios, wherein the texts to be processed are known in advance. However, despite its practical advantages, transductive learning is underexplored in natural language processing. Here we conduct an empirical study of transductive learning for neural models and demonstrate its utility in syntactic and semantic tasks. Specifically, we fine-tune language models (LMs) on an unlabeled test set to obtain test-set-specific word representations. Through extensive experiments, we demonstrate that despite its simplicity, transductive LM fine-tuning consistently improves state-of-the-art neural models in in-domain and out-of-domain settings." @default.
• W2970975962 created "2019-09-05" @default.
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• W2970975962 date "2019-01-01" @default.
• W2970975962 modified "2023-09-23" @default.
• W2970975962 title "Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis" @default.
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• W2970975962 doi "https://doi.org/10.18653/v1/d19-1379" @default.
• W2970975962 hasPublicationYear "2019" @default.
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