MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

Jiaao Chen, Zichao Yang, Diyi Yang


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
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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.194.pdf
Video:
 http://slideslive.com/38929239
Code
 GT-SALT/MixText
Data
AG NewsIMDb Movie Reviews