MixText

Year: 2,020
Journal: Annual Meeting of the Association for Computational Linguistics
Languages: English
Programming languages: Python
Input data:

sentences

This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess low-entropy labels for unlabeled data, hence making them as easy to use as labeled data.

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