Version 1
: Received: 21 March 2022 / Approved: 23 March 2022 / Online: 23 March 2022 (02:28:21 CET)
How to cite:
Afram, G.K.; Weyori, B.A.; Adekoya, F.A. TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language. Preprints2022, 2022030303. https://doi.org/10.20944/preprints202203.0303.v1
Afram, G.K.; Weyori, B.A.; Adekoya, F.A. TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language. Preprints 2022, 2022030303. https://doi.org/10.20944/preprints202203.0303.v1
Afram, G.K.; Weyori, B.A.; Adekoya, F.A. TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language. Preprints2022, 2022030303. https://doi.org/10.20944/preprints202203.0303.v1
APA Style
Afram, G.K., Weyori, B.A., & Adekoya, F.A. (2022). TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language. Preprints. https://doi.org/10.20944/preprints202203.0303.v1
Chicago/Turabian Style
Afram, G.K., Benjamin Asubam Weyori and Felix Adebayo Adekoya. 2022 "TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language" Preprints. https://doi.org/10.20944/preprints202203.0303.v1
Abstract
A Twi-English parallel corpus is certainly an important resource for Machine Translation of Twi (ISO 639-3), a Low-Resource African Language (LRAL) which is mainly spoken in Ghana and Ivory Coast. Currently large-scale multi-domain Twi-English parallel corpus is still unavailable partly due to the difficulties and the arduous efforts required in its design. In this paper, we present TWIENG: a large-scale multi-domain Twi-English parallel corpus. We crawled the sentences from the web using web crawlers, translated, aligned, tokenized and compiled to create the corpus. We crawled English sentences from Ghanaian indigenous electronic news portals, Ghanaian Parliamentary Hansards, Twi Bible and crowdsourcing via google forms. The sentences were translated by professional translators and linguists, they were then aligned, tokenized and compiled. The corpus was curated using the sketch engine, a corpus manager and analysis software developed by Lexical Computing Limited. The corpus was manually evaluated by Twi professional linguists. The Corpus has 5,419 parallel sentences.
Keywords
Twi; Parallel corpus; Tokens; Sketch Engine; Word sketch; Parallel concordance; Machine Translation; Low-Resource Language
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
