Mitigating Translationese in Low-resource Languages: The Storyboard Approach
| Autor: | Kuwanto, Garry, Urua, Eno-Abasi E., Amuok, Priscilla Amondi, Muhammad, Shamsuddeen Hassan, Aremu, Anuoluwapo, Otiende, Verrah, Nanyanga, Loice Emma, Nyoike, Teresiah W., Akpan, Aniefon D., Udouboh, Nsima Ab, Archibong, Idongesit Udeme, Moses, Idara Effiong, Ige, Ifeoluwatayo A., Ajibade, Benjamin, Awokoya, Olumide Benjamin, Abdulmumin, Idris, Aliyu, Saminu Mohammad, Iro, Ruqayya Nasir, Ahmad, Ibrahim Said, Smith, Deontae, Michaels, Praise-EL, Adelani, David Ifeoluwa, Wijaya, Derry Tanti, Andy, Anietie |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) 11349-11360 |
| Druh dokumentu: | Working Paper |
| Popis: | Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused. Comment: published at LREC-COLING 2024 |
| Databáze: | arXiv |
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