Evaluating Amharic Machine Translation
This provides a benchmark for researchers and practitioners working on Amharic machine translation, though it is incremental as it focuses on evaluation rather than new methods.
The paper tackled the problem of evaluating machine translation for the low-resource language Amharic by developing and sharing a dataset, and found that current commercial systems show promising but low BLEU scores.
Machine translation (MT) systems are now able to provide very accurate results for high resource language pairs. However, for many low resource languages, MT is still under active research. In this paper, we develop and share a dataset to automatically evaluate the quality of MT systems for Amharic. We compare two commercially available MT systems that support translation of Amharic to and from English to assess the current state of MT for Amharic. The BLEU score results show that the results for Amharic translation are promising but still low. We hope that this dataset will be useful to the research community both in academia and industry as a benchmark to evaluate Amharic MT systems.