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Crowdsourcing Parallel Corpus for English-Oromo Neural Machine Translation using Community Engagement Platform

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arxiv 2102.07539 v1 pith:2NBRBLRH submitted 2021-02-15 cs.AI

Sisay Chala , Bekele Debisa , Amante Diriba , Silas Getachew , Chala Getu , Solomon Shiferaw This is my paper · ORCID
classification cs.AI
keywords corpuslanguagemachinetranslationafaanamountcollectedcommunity
verification ladder T0 review T1 audit T2 compute T3 formal
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Even though Afaan Oromo is the most widely spoken language in the Cushitic family by more than fifty million people in the Horn and East Africa, it is surprisingly resource-scarce from a technological point of view. The increasing amount of various useful documents written in English language brings to investigate the machine that can translate those documents and make it easily accessible for local language. The paper deals with implementing a translation of English to Afaan Oromo and vice versa using Neural Machine Translation. But the implementation is not very well explored due to the limited amount and diversity of the corpus. However, using a bilingual corpus of just over 40k sentence pairs we have collected, this study showed a promising result. About a quarter of this corpus is collected via Community Engagement Platform (CEP) that was implemented to enrich the parallel corpus through crowdsourcing translations.

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  1. Sagalee: an Open Source Automatic Speech Recognition Dataset for Oromo Language

    cs.CL 2025-02 conditional novelty 6.0 of 10

    The paper releases Sagalee, a 100-hour, 283-speaker Oromo ASR dataset, and reports baseline WERs of 15.32% (Conformer AED), 18.74% (Conformer CTC), and 10.82% (Whisper Large-v3 fine-tuned).

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