@inproceedings{dash-etal-2023-bits,
title = "{BITS}-{P} at {WAT} 2023: Improving {I}ndic Language Multimodal Translation by Image Augmentation using Diffusion Models",
author = "Dash, Amulya and
Gupta, Hrithik Raj and
Sharma, Yashvardhan",
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-1.3",
pages = "41--45",
abstract = "This paper describes the proposed system for mutlimodal machine translation. We have participated in multimodal translation tasks for English into three Indic languages: Hindi, Bengali, and Malayalam. We leverage the inherent richness of multimodal data to bridge the gap of ambiguity in translation. We fine-tuned the {`}No Language Left Behind{'} (NLLB) machine translation model for multimodal translation, further enhancing the model accuracy by image data augmentation using latent diffusion. Our submission achieves the best BLEU score for English-Hindi, English-Bengali, and English-Malayalam language pairs for both Evaluation and Challenge test sets.",
}
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%0 Conference Proceedings
%T BITS-P at WAT 2023: Improving Indic Language Multimodal Translation by Image Augmentation using Diffusion Models
%A Dash, Amulya
%A Gupta, Hrithik Raj
%A Sharma, Yashvardhan
%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 dash-etal-2023-bits
%X This paper describes the proposed system for mutlimodal machine translation. We have participated in multimodal translation tasks for English into three Indic languages: Hindi, Bengali, and Malayalam. We leverage the inherent richness of multimodal data to bridge the gap of ambiguity in translation. We fine-tuned the ‘No Language Left Behind’ (NLLB) machine translation model for multimodal translation, further enhancing the model accuracy by image data augmentation using latent diffusion. Our submission achieves the best BLEU score for English-Hindi, English-Bengali, and English-Malayalam language pairs for both Evaluation and Challenge test sets.
%U https://aclanthology.org/2023.wat-1.3
%P 41-45
Markdown (Informal)
[BITS-P at WAT 2023: Improving Indic Language Multimodal Translation by Image Augmentation using Diffusion Models](https://aclanthology.org/2023.wat-1.3) (Dash et al., WAT 2023)
ACL