The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation

Jonne Saleva, Constantine Lignos


Abstract
This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting. We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL. We evaluate translation tasks between English and each of Nepali, Sinhala, and Kazakh, and predict that using morphologically-based segmentation methods would lead to better performance in this setting. However, comparing to BPE, we find that no consistent and reliable differences emerge between the segmentation methods. While morphologically-based methods outperform BPE in a few cases, what performs best tends to vary across tasks, and the performance of segmentation methods is often statistically indistinguishable.
Anthology ID:
2021.eacl-srw.22
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
164–174
Language:
URL:
https://aclanthology.org/2021.eacl-srw.22
DOI:
10.18653/v1/2021.eacl-srw.22
Bibkey:
Cite (ACL):
Jonne Saleva and Constantine Lignos. 2021. The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 164–174, Online. Association for Computational Linguistics.
Cite (Informal):
The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation (Saleva & Lignos, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-srw.22.pdf
Data
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