Andargachew Mekonnen Gezmu


Extended Parallel Corpus for Amharic-English Machine Translation
Andargachew Mekonnen Gezmu | Andreas Nürnberger | Tesfaye Bayu Bati
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be helpful for machine translation of a low-resource language, Amharic. We freely released the corpus for research purposes. Furthermore, we developed baseline statistical and neural machine translation systems; we trained statistical and neural machine translation models using the corpus. In the experiments, we also used a large monolingual corpus for the language model of statistical machine translation and back-translation of neural machine translation. In the automatic evaluation, neural machine translation models outperform statistical machine translation models by approximately six to seven Bilingual Evaluation Understudy (BLEU) points. Besides, among the neural machine translation models, the subword models outperform the word-based models by three to four BLEU points. Moreover, two other relevant automatic evaluation metrics, Translation Edit Rate on Character Level and Better Evaluation as Ranking, reflect corresponding differences among the trained models.


Portable Spelling Corrector for a Less-Resourced Language: Amharic
Andargachew Mekonnen Gezmu | Andreas Nürnberger | Binyam Ephrem Seyoum
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Contemporary Amharic Corpus: Automatically Morpho-Syntactically Tagged Amharic Corpus
Andargachew Mekonnen Gezmu | Binyam Ephrem Seyoum | Michael Gasser | Andreas Nürnberger
Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing

We introduced the contemporary Amharic corpus, which is automatically tagged for morpho-syntactic information. Texts are collected from 25,199 documents from different domains and about 24 million orthographic words are tokenized. Since it is partly a web corpus, we made some automatic spelling error correction. We have also modified the existing morphological analyzer, HornMorpho, to use it for the automatic tagging.