TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language

Afram, Gabriel Kwadwo; Weyori, Benjamin Asubam; Adekoya, Adebayo Felix

doi:10.20944/preprints202203.0303.v1

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Students have thus been known to complete high-quality assessments without putting any significant effort -in this regard, language lecturers sometimes witness work submissions that contain errors from AI-software-generated tools (Rudolph et al, 2023;Sharma et al, 2022). Moreover, while these AI tools have improved significantly in recent years, they are not always accurate and can produce awkward or nonsensical translations (Afram et al, 2022). In these cases, language students in developing contexts sometimes use AI to cheat by using machine translation tools to translate their assignments from their native language to the target language (Klimova et al, 2023;Shiri, 2023;Straume & Anson, 2022).…”

Section: Academic Dishonesty On Steroidsmentioning

confidence: 99%

Language Education and Artificial Intelligence: An Exploration of Challenges Confronting Academics in Global South Universities

Makeleni

Mutongoza

Linake

2023

JCVE

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While the global South universities have made significant strides in adopting digital technologies, there remain huge gaps, particularly when it comes to the acceptance of artificial intelligence (AI) in institutions of higher learning. As such, this study sought to explore global South academics’ reported AI-related challenges in the language education domain from published literature. To achieve this, the researchers employed a literature review methodology which entailed meticulous searches for published literature using key words. The challenges reported in literature revealed four broad challenges namely limited language options, academic dishonesty, biases and lack of accountability, and laziness among students and lecturers. Based on these findings, the study recommended that there be an urgent prioritisation of the development of AI-based language education tools that are specifically tailored to the needs and contexts of learners in the global South. The study also recommended the development of accessible and affordable AI-based language education tools, that will promote the development of digital literacy skills among educators and learners in the global South.

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Section: Academic Dishonesty On Steroidsmentioning

confidence: 99%

Language Education and Artificial Intelligence: An Exploration of Challenges Confronting Academics in Global South Universities

Makeleni

Mutongoza

Linake

2023

JCVE

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“…The authors of [39] present the Twieng corpus, a small English-Twi parallel corpus with 5,419 sentences. The corpus is based on online news portals, Twi literature, the Ghanaian Parliamentary Hansard, the Twi-English Bible, and Social Media crowdsourcing and has a focus on socio-cultural, educational and legal issues.…”

Section: Parallel Corpora For Twimentioning

confidence: 99%

Twi Machine Translation

Gyasi,

Schlippe

2023

BDCC

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French is a strategically and economically important language in the regions where the African language Twi is spoken. However, only a very small proportion of Twi speakers in Ghana speak French. The development of a Twi–French parallel corpus and corresponding machine translation applications would provide various advantages, including stimulating trade and job creation, supporting the Ghanaian diaspora in French-speaking nations, assisting French-speaking tourists and immigrants seeking medical care in Ghana, and facilitating numerous downstream natural language processing tasks. Since there are hardly any machine translation systems or parallel corpora between Twi and French that cover a modern and versatile vocabulary, our goal was to extend a modern Twi–English corpus with French and develop machine translation systems between Twi and French: Consequently, in this paper, we present our Twi–French corpus of 10,708 parallel sentences. Furthermore, we describe our machine translation experiments with this corpus. We investigated direct machine translation and cascading systems that use English as a pivot language. Our best Twi–French system is a direct state-of-the-art transformer-based machine translation system that achieves a BLEU score of 0.76. Our best French–Twi system, which is a cascading system that uses English as a pivot language, results in a BLEU score of 0.81. Both systems are fine tuned with our corpus, and our French–Twi system even slightly outperforms Google Translate on our test set by 7% relative.

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Low resource Twi-English parallel corpus for machine translation in multiple domains (Twi-2-ENG)

Agyei,

Zhang,

Bannerman

et al. 2024

Discov Computing

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Although Ghana does not have one unique language for its citizens, the Twi dialect stands a chance of fulfilling this purpose. Twi is among the low-resourced language categories, yet it is widely spoken beyond Ghana and in countries such as the Ivory Coast, Benin, Nigeria, and other places. However, it continues to be seen as the perfect resource for Twi Machine Translation (MT) of IS0 639-3. The issue with the Twi-English parallel corpus is eminent at the multiple domain dataset level, partly due to the complex design structure and scarcity of the digital Twi lexicon. This study introduced Twi-2-ENG, a large-scale multiple domain Twi to English parallel corpus, Twi digital Dictionary, and lexicon version of Twi. Also, it employed the Ghanaian Parliamentary Hansards, crowdsourcing, and digital Ghana News Portals to crawl all the English sentences. Our curled news portals accumulated 5,765 parallel corpus sentences, the Twi New Testament Bible, and social media platforms. The data-gathering method used means of translation, compilation, tokenization, and the final alignments with the Twi-English parallel sentences, including the technology employed in compiling and hosting the corpus, were duly discussed. The results reveal that the role of manually qualified linguistic professionals and Twi translation specialists across the media spectrum, academia, and well-wishers adds a considerable volume to the Twi-2-ENG parallel corpus. Finally, all the sentences were curated with the help of a corpus manager, sketch engine, linguistics, and professional translators to align and tokenize all texts, allowing the Twi professional linguists to evaluate the corpus.

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TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language

Cited by 3 publications

References 9 publications

Language Education and Artificial Intelligence: An Exploration of Challenges Confronting Academics in Global South Universities

Language Education and Artificial Intelligence: An Exploration of Challenges Confronting Academics in Global South Universities

Twi Machine Translation

Low resource Twi-English parallel corpus for machine translation in multiple domains (Twi-2-ENG)

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