The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation

Abinew Ali Ayele; Alexander Gelbukh; Atnafu Lambebo Tonja; Grigori Sidorov; Olga Kolesnikova; Seid Muhie Yimam; Silesh Bogale Haile; Tadesse Destaw Belay

The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2210.15224 v1 pith:DJAUM2KX submitted 2022-10-27 cs.CL

Tadesse Destaw Belay , Atnafu Lambebo Tonja , Olga Kolesnikova , Seid Muhie Yimam , Abinew Ali Ayele , Silesh Bogale Haile , Grigori Sidorov , Alexander Gelbukh This is my paper · ORCID

classification cs.CL

keywords translationamharic-englishmachinenormalizationamharicbi-directionaldatahomophone

verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments

read the original abstract

Machine translation (MT) is one of the main tasks in natural language processing whose objective is to translate texts automatically from one natural language to another. Nowadays, using deep neural networks for MT tasks has received great attention. These networks require lots of data to learn abstract representations of the input and store it in continuous vectors. This paper presents the first relatively large-scale Amharic-English parallel sentence dataset. Using these compiled data, we build bi-directional Amharic-English translation models by fine-tuning the existing Facebook M2M100 pre-trained model achieving a BLEU score of 37.79 in Amharic-English 32.74 in English-Amharic translation. Additionally, we explore the effects of Amharic homophone normalization on the machine translation task. The results show that the normalization of Amharic homophone characters increases the performance of Amharic-English machine translation in both directions.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge'ez Script
cs.CL 2025-07 conditional novelty 6.0 of 10

Homophone normalization in Amharic training data can harm cross-lingual machine translation to Tigrinya and Ge'ez, but applying the normalization only at scoring time recovers BLEU gains.

Pith tools