Methods › Natural Language Processing › Text Augmentation › MixText

MixText

3 papers tagged archive 2025-07-28

Introduced by Jiaao Chen et al. in MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

MixText is a semi-supervised learning method for text classification, which uses a new data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. The technique leverages advances in data augmentation to guess low-entropy labels for unlabeled data, making them as easy to use as labeled data.

PaperSource

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Data Augmentation2
Binary text classification1
Classification1
General Classification1
Semi-Supervised Text Classification1
Text Classification1

Usage over time archive 2025-07-28

Papers per year tagged with MixText: 2020 to 2024, peak 2 2 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Text AugmentationText Classification ModelsSemi-Supervised Learning Methods

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