@inproceedings{shahid-etal-2023-odiagenais,
title = "{O}dia{G}en{AI}{'}s Participation at {WAT}2023",
author = {Shahid, Sk and
Kohli, Guneet Singh and
Sekhar, Sambit and
Dhal, Debasish and
Sharma, Adit and
Kushwaha, Shubhendra and
Parida, Shantipriya and
Gr{\"o}nroos, Stig-Arne and
Dash, Satya Ranjan},
editor = "Nakazawa, Toshiaki and
Kinugawa, Kazutaka and
Mino, Hideya and
Goto, Isao and
Dabre, Raj and
Higashiyama, Shohei and
Parida, Shantipriya and
Morishita, Makoto and
Bojar, Ondrej and
Eriguchi, Akiko and
Oda, Yusuke and
Eriguchi, Akiko and
Chu, Chenhui and
Kurohashi, Sadao",
booktitle = "Proceedings of the 10th Workshop on Asian Translation",
month = sep,
year = "2023",
address = "Macau SAR, China",
publisher = "Asia-Pacific Association for Machine Translation",
url = "https://aclanthology.org/2023.wat-1.4",
pages = "46--52",
abstract = "This paper offers an in-depth overview of the team {``}ODIAGEN{'}s{''} translation system submitted to the Workshop on Asian Translation (WAT2023). Our focus lies in the domain of Indic Multimodal tasks, specifically targeting English to Hindi, English to Malayalam, and English to Bengali translations. The system uses a state-of-the-art Transformer-based architecture, specifically the NLLB-200 model, fine-tuned with language-specific Visual Genome Datasets. With this robust system, we were able to manage both text-to-text and multimodal translations, demonstrating versatility in handling different translation modes. Our results showcase strong performance across the board, with particularly promising results in the Hindi and Bengali translation tasks. A noteworthy achievement of our system lies in its stellar performance across all text-to-text translation tasks. In the categories of English to Hindi, English to Bengali, and English to Malayalam translations, our system claimed the top positions for both the evaluation and challenge sets. This system not only advances our understanding of the challenges and nuances of Indic language translation but also opens avenues for future research to enhance translation accuracy and performance.",
}
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<abstract>This paper offers an in-depth overview of the team “ODIAGEN’s” translation system submitted to the Workshop on Asian Translation (WAT2023). Our focus lies in the domain of Indic Multimodal tasks, specifically targeting English to Hindi, English to Malayalam, and English to Bengali translations. The system uses a state-of-the-art Transformer-based architecture, specifically the NLLB-200 model, fine-tuned with language-specific Visual Genome Datasets. With this robust system, we were able to manage both text-to-text and multimodal translations, demonstrating versatility in handling different translation modes. Our results showcase strong performance across the board, with particularly promising results in the Hindi and Bengali translation tasks. A noteworthy achievement of our system lies in its stellar performance across all text-to-text translation tasks. In the categories of English to Hindi, English to Bengali, and English to Malayalam translations, our system claimed the top positions for both the evaluation and challenge sets. This system not only advances our understanding of the challenges and nuances of Indic language translation but also opens avenues for future research to enhance translation accuracy and performance.</abstract>
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%0 Conference Proceedings
%T OdiaGenAI’s Participation at WAT2023
%A Shahid, Sk
%A Kohli, Guneet Singh
%A Sekhar, Sambit
%A Dhal, Debasish
%A Sharma, Adit
%A Kushwaha, Shubhendra
%A Parida, Shantipriya
%A Grönroos, Stig-Arne
%A Dash, Satya Ranjan
%Y Nakazawa, Toshiaki
%Y Kinugawa, Kazutaka
%Y Mino, Hideya
%Y Goto, Isao
%Y Dabre, Raj
%Y Higashiyama, Shohei
%Y Parida, Shantipriya
%Y Morishita, Makoto
%Y Bojar, Ondrej
%Y Eriguchi, Akiko
%Y Oda, Yusuke
%Y Chu, Chenhui
%Y Kurohashi, Sadao
%S Proceedings of the 10th Workshop on Asian Translation
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F shahid-etal-2023-odiagenais
%X This paper offers an in-depth overview of the team “ODIAGEN’s” translation system submitted to the Workshop on Asian Translation (WAT2023). Our focus lies in the domain of Indic Multimodal tasks, specifically targeting English to Hindi, English to Malayalam, and English to Bengali translations. The system uses a state-of-the-art Transformer-based architecture, specifically the NLLB-200 model, fine-tuned with language-specific Visual Genome Datasets. With this robust system, we were able to manage both text-to-text and multimodal translations, demonstrating versatility in handling different translation modes. Our results showcase strong performance across the board, with particularly promising results in the Hindi and Bengali translation tasks. A noteworthy achievement of our system lies in its stellar performance across all text-to-text translation tasks. In the categories of English to Hindi, English to Bengali, and English to Malayalam translations, our system claimed the top positions for both the evaluation and challenge sets. This system not only advances our understanding of the challenges and nuances of Indic language translation but also opens avenues for future research to enhance translation accuracy and performance.
%U https://aclanthology.org/2023.wat-1.4
%P 46-52
Markdown (Informal)
[OdiaGenAI’s Participation at WAT2023](https://aclanthology.org/2023.wat-1.4) (Shahid et al., WAT 2023)
ACL
- Sk Shahid, Guneet Singh Kohli, Sambit Sekhar, Debasish Dhal, Adit Sharma, Shubhendra Kushwaha, Shantipriya Parida, Stig-Arne Grönroos, and Satya Ranjan Dash. 2023. OdiaGenAI’s Participation at WAT2023. In Proceedings of the 10th Workshop on Asian Translation, pages 46–52, Macau SAR, China. Asia-Pacific Association for Machine Translation.