Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic
Abstract
We present the second ever evaluated Arabic dialect-to-dialect machine translation effort, and the first to leverage external resources beyond a small parallel corpus. The subject has not previously received serious attention due to lack of naturally occurring parallel data; yet its importance is evidenced by dialectal Arabic's wide usage and breadth of inter-dialect variation, comparable to that of Romance languages. Our results suggest that modeling morphology and syntax significantly improves dialect-to-dialect translation, though optimizing such data-sparse models requires consideration of the linguistic differences between dialects and the nature of available data and resources. On a single-reference blind test set where untranslated input scores 6.5 BLEU and a model trained only on parallel data reaches 14.6, pivot techniques and morphosyntactic modeling significantly improve performance to 17.5.
Keywords
Cite
@article{arxiv.1712.06273,
title = {Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic},
author = {Alexander Erdmann and Nizar Habash and Dima Taji and Houda Bouamor},
journal= {arXiv preprint arXiv:1712.06273},
year = {2017}
}