@inproceedings{kuwanto-etal-2024-mitigating,
    title = "Mitigating Translationese in Low-resource Languages: The Storyboard Approach",
    author = "Kuwanto, Garry  and
      Urua, Eno-Abasi E.  and
      Amuok, Priscilla Amondi  and
      Muhammad, Shamsuddeen Hassan  and
      Aremu, Anuoluwapo  and
      Otiende, Verrah  and
      Nanyanga, Loice Emma  and
      Nyoike, Teresiah W.  and
      Akpan, Aniefon D.  and
      Udouboh, Nsima Ab  and
      Archibong, Idongesit Udeme  and
      Moses, Idara Effiong  and
      Ige, Ifeoluwatayo A.  and
      Ajibade, Benjamin  and
      Awokoya, Olumide Benjamin  and
      Abdulmumin, Idris  and
      Aliyu, Saminu Mohammad  and
      Iro, Ruqayya Nasir  and
      Ahmad, Ibrahim Said  and
      Smith, Deontae  and
      Michaels, Praise-EL  and
      Adelani, David Ifeoluwa  and
      Wijaya, Derry Tanti  and
      Andy, Anietie",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.992",
    pages = "11349--11360",
    abstract = "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.",
}

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.
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
Mitigating Translationese in Low-resource Languages: The Storyboard Approach