Abstract
Data augmentation is an effective approach to tackle over-fitting. Many previous works have proposed different data augmentations strategies for NLP, such as noise injection, word replacement, back-translation etc. Though effective, they missed one important characteristic of language–compositionality, meaning of a complex expression is built from its sub-parts. Motivated by this, we propose a compositional data augmentation approach for natural language understanding called TreeMix. Specifically, TreeMix leverages constituency parsing tree to decompose sentences into constituent sub-structures and the Mixup data augmentation technique to recombine them to generate new sentences. Compared with previous approaches, TreeMix introduces greater diversity to the samples generated and encourages models to learn compositionality of NLP data. Extensive experiments on text classification and SCAN demonstrate that TreeMix outperforms current state-of-the-art data augmentation methods.- Anthology ID:
- 2022.naacl-main.385
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5243–5258
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.385
- DOI:
- 10.18653/v1/2022.naacl-main.385
- Cite (ACL):
- Le Zhang, Zichao Yang, and Diyi Yang. 2022. TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5243–5258, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding (Zhang et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.naacl-main.385.pdf
- Code
- magiccircuit/treemix
- Data
- AG News, GLUE, QNLI, SCAN