Pushing the Limits of Low-Resource Morphological Inflection

Antonios Anastasopoulos, Graham Neubig


Abstract
Recent years have seen exceptional strides in the task of automatic morphological inflection generation. However, for a long tail of languages the necessary resources are hard to come by, and state-of-the-art neural methods that work well under higher resource settings perform poorly in the face of a paucity of data. In response, we propose a battery of improvements that greatly improve performance under such low-resource conditions. First, we present a novel two-step attention architecture for the inflection decoder. In addition, we investigate the effects of cross-lingual transfer from single and multiple languages, as well as monolingual data hallucination. The macro-averaged accuracy of our models outperforms the state-of-the-art by 15 percentage points. Also, we identify the crucial factors for success with cross-lingual transfer for morphological inflection: typological similarity and a common representation across languages.
Anthology ID:
D19-1091
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
984–996
Language:
URL:
https://aclanthology.org/D19-1091
DOI:
10.18653/v1/D19-1091
Bibkey:
Cite (ACL):
Antonios Anastasopoulos and Graham Neubig. 2019. Pushing the Limits of Low-Resource Morphological Inflection. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 984–996, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Pushing the Limits of Low-Resource Morphological Inflection (Anastasopoulos & Neubig, EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1091.pdf
Code
 antonisa/inflection +  additional community code