Interplay of Machine Translation, Diacritics, and Diacritization
Wei-Rui Chen Ife Adebara Muhammad Abdul-Mageed
We investigate two research questions: (1) how do machine translation (MT)
and diacritization influence the performance of each other in a multi-task
learning setting (2) the effect of keeping (vs. removing) diacritics on MT
performance. We examine these two questions in both high-resource (HR) and
low-resource (LR) settings across 55 different languages (36 African languages
and 19 European languages). For (1), results show that diacritization
significantly benefits MT in the LR scenario, doubling or even tripling
performance for some languages, but harms MT in the HR scenario. We find that
MT harms diacritization in LR but benefits significantly in HR for some
languages. For (2), MT performance is similar regardless of diacritics being
kept or removed. In addition, we propose two classes of metrics to measure the
complexity of a diacritical system, finding these metrics to correlate
positively with the performance of our diacritization models. Overall, our work
provides insights for developing MT and diacritization systems under different
data size conditions and may have implications that generalize beyond the 55
languages we investigate.