@inproceedings{kaino-etal-2024-utilizing-longer,
title = "Utilizing Longer Context than Speech Bubbles in Automated Manga Translation",
author = "Kaino, Hiroto and
Sugihara, Soichiro and
Kajiwara, Tomoyuki and
Ninomiya, Takashi and
Tanner, Joshua B. and
Ishiwatari, Shonosuke",
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.1505",
pages = "17337--17342",
abstract = "This paper focuses on improving the performance of machine translation for manga (Japanese-style comics). In manga machine translation, text consists of a sequence of speech bubbles and each speech bubble is translated individually. However, each speech bubble itself does not contain sufficient information for translation. Therefore, previous work has proposed methods to use contextual information, such as the previous speech bubble, speech bubbles within the same scene, and corresponding scene images. In this research, we propose two new approaches to capture broader contextual information. Our first approach involves scene-based translation that considers the previous scene. The second approach considers broader context information, including details about the work, author, and manga genre. Through our experiments, we confirm that each of our methods improves translation quality, with the combination of both methods achieving the highest quality. Additionally, detailed analysis reveals the effect of zero-anaphora resolution in translation, such as supplying missing subjects not mentioned within a scene, highlighting the usefulness of longer contextual information in manga machine translation.",
}
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<abstract>This paper focuses on improving the performance of machine translation for manga (Japanese-style comics). In manga machine translation, text consists of a sequence of speech bubbles and each speech bubble is translated individually. However, each speech bubble itself does not contain sufficient information for translation. Therefore, previous work has proposed methods to use contextual information, such as the previous speech bubble, speech bubbles within the same scene, and corresponding scene images. In this research, we propose two new approaches to capture broader contextual information. Our first approach involves scene-based translation that considers the previous scene. The second approach considers broader context information, including details about the work, author, and manga genre. Through our experiments, we confirm that each of our methods improves translation quality, with the combination of both methods achieving the highest quality. Additionally, detailed analysis reveals the effect of zero-anaphora resolution in translation, such as supplying missing subjects not mentioned within a scene, highlighting the usefulness of longer contextual information in manga machine translation.</abstract>
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%0 Conference Proceedings
%T Utilizing Longer Context than Speech Bubbles in Automated Manga Translation
%A Kaino, Hiroto
%A Sugihara, Soichiro
%A Kajiwara, Tomoyuki
%A Ninomiya, Takashi
%A Tanner, Joshua B.
%A Ishiwatari, Shonosuke
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F kaino-etal-2024-utilizing-longer
%X This paper focuses on improving the performance of machine translation for manga (Japanese-style comics). In manga machine translation, text consists of a sequence of speech bubbles and each speech bubble is translated individually. However, each speech bubble itself does not contain sufficient information for translation. Therefore, previous work has proposed methods to use contextual information, such as the previous speech bubble, speech bubbles within the same scene, and corresponding scene images. In this research, we propose two new approaches to capture broader contextual information. Our first approach involves scene-based translation that considers the previous scene. The second approach considers broader context information, including details about the work, author, and manga genre. Through our experiments, we confirm that each of our methods improves translation quality, with the combination of both methods achieving the highest quality. Additionally, detailed analysis reveals the effect of zero-anaphora resolution in translation, such as supplying missing subjects not mentioned within a scene, highlighting the usefulness of longer contextual information in manga machine translation.
%U https://aclanthology.org/2024.lrec-main.1505
%P 17337-17342
Markdown (Informal)
[Utilizing Longer Context than Speech Bubbles in Automated Manga Translation](https://aclanthology.org/2024.lrec-main.1505) (Kaino et al., LREC-COLING 2024)
ACL
- Hiroto Kaino, Soichiro Sugihara, Tomoyuki Kajiwara, Takashi Ninomiya, Joshua B. Tanner, and Shonosuke Ishiwatari. 2024. Utilizing Longer Context than Speech Bubbles in Automated Manga Translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17337–17342, Torino, Italia. ELRA and ICCL.