Abstract
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. By mixing labeled, unlabeled and augmented data, MixText significantly outperformed current pre-trained and fined-tuned models and other state-of-the-art semi-supervised learning methods on several text classification benchmarks. The improvement is especially prominent when supervision is extremely limited. We have publicly released our code at https://github.com/GT-SALT/MixText.- Anthology ID:
- 2020.acl-main.194
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2147–2157
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.194
- DOI:
- 10.18653/v1/2020.acl-main.194
- Cite (ACL):
- Jiaao Chen, Zichao Yang, and Diyi Yang. 2020. MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2147–2157, Online. Association for Computational Linguistics.
- Cite (Informal):
- MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification (Chen et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.194.pdf
- Code
- GT-SALT/MixText + additional community code
- Data
- AG News, IMDb Movie Reviews