Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text

Marwa Gaser, Manuel Mager, Injy Hamed, Nizar Habash, Slim Abdennadher, Ngoc Thang Vu


Abstract
Data sparsity is one of the main challenges posed by code-switching (CS), which is further exacerbated in the case of morphologically rich languages. For the task of machine translation (MT), morphological segmentation has proven successful in alleviating data sparsity in monolingual contexts; however, it has not been investigated for CS settings. In this paper, we study the effectiveness of different segmentation approaches on MT performance, covering morphology-based and frequency-based segmentation techniques. We experiment on MT from code-switched Arabic-English to English. We provide detailed analysis, examining a variety of conditions, such as data size and sentences with different degrees of CS. Empirical results show that morphology-aware segmenters perform the best in segmentation tasks but under-perform in MT. Nevertheless, we find that the choice of the segmentation setup to use for MT is highly dependent on the data size. For extreme low-resource scenarios, a combination of frequency and morphology-based segmentations is shown to perform the best. For more resourced settings, such a combination does not bring significant improvements over the use of frequency-based segmentation.
Anthology ID:
2023.eacl-main.256
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3523–3538
Language:
URL:
https://aclanthology.org/2023.eacl-main.256
DOI:
10.18653/v1/2023.eacl-main.256
Bibkey:
Cite (ACL):
Marwa Gaser, Manuel Mager, Injy Hamed, Nizar Habash, Slim Abdennadher, and Ngoc Thang Vu. 2023. Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3523–3538, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text (Gaser et al., EACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eacl-main.256.pdf
Dataset:
 2023.eacl-main.256.dataset.zip
Video:
 https://aclanthology.org/2023.eacl-main.256.mp4