Abstract
The lack of annotated data is a big issue for building reliable NLP systems for most of the world’s languages. But this problem can be alleviated by automatic data generation. In this paper, we present a new data augmentation method for artificially creating new dependency-annotated sentences. The main idea is to swap subtrees between annotated sentences while enforcing strong constraints on those trees to ensure maximal grammaticality of the new sentences. We also propose a method to perform low-resource experiments using resource-rich languages by mimicking low-resource languages by sampling sentences under a low-resource distribution. In a series of experiments, we show that our newly proposed data augmentation method outperforms previous proposals using the same basic inputs.- Anthology ID:
- 2020.coling-main.339
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3818–3830
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.339
- DOI:
- 10.18653/v1/2020.coling-main.339
- Cite (ACL):
- Mathieu Dehouck and Carlos Gómez-Rodríguez. 2020. Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3818–3830, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages (Dehouck & Gómez-Rodríguez, COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.339.pdf