Data Augmentation via Dependency Tree Morphing for Low-Resource Languages

Gözde Gül Şahin, Mark Steedman


Abstract
Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We “crop” sentences by removing dependency links, and we “rotate” sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.
Anthology ID:
D18-1545
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5004–5009
Language:
URL:
https://aclanthology.org/D18-1545
DOI:
10.18653/v1/D18-1545
Bibkey:
Cite (ACL):
Gözde Gül Şahin and Mark Steedman. 2018. Data Augmentation via Dependency Tree Morphing for Low-Resource Languages. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 5004–5009, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages (Şahin & Steedman, EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ml4al-ingestion/D18-1545.pdf
Code
 gozdesahin/crop-rotate-augment +  additional community code