@inproceedings{deoghare-etal-2024-together,
title = "Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages",
author = "Deoghare, Sourabh and
Kanojia, Diptesh and
Bhattacharyya, Pushpak",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.634",
pages = "10800--10812",
abstract = "This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages. Focusing on two closely related language pairs, English-Marathi and English-Hindi, we exploit the linguistic similarities to develop a robust multilingual APE model. To facilitate cross-linguistic transfer, we generate synthetic Hindi-Marathi and Marathi-Hindi APE triplets. Additionally, we incorporate a Quality Estimation (QE)-APE multi-task learning framework. While the experimental results underline the complementary nature of APE and QE, we also observe that QE-APE multitask learning facilitates effective domain adaptation. Our experiments demonstrate that the multilingual APE models outperform their corresponding English-Hindi and English-Marathi single-pair models by 2.5 and 2.39 TER points, respectively, with further notable improvements over the multilingual APE model observed through multi-task learning ($+1.29$ and $+1.44$ TER points), data augmentation ($+0.53$ and $+0.45$ TER points) and domain adaptation ($+0.35$ and $+0.45$ TER points). We release the synthetic data, code, and models accrued during this study publicly for further research.",
}
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<abstract>This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages. Focusing on two closely related language pairs, English-Marathi and English-Hindi, we exploit the linguistic similarities to develop a robust multilingual APE model. To facilitate cross-linguistic transfer, we generate synthetic Hindi-Marathi and Marathi-Hindi APE triplets. Additionally, we incorporate a Quality Estimation (QE)-APE multi-task learning framework. While the experimental results underline the complementary nature of APE and QE, we also observe that QE-APE multitask learning facilitates effective domain adaptation. Our experiments demonstrate that the multilingual APE models outperform their corresponding English-Hindi and English-Marathi single-pair models by 2.5 and 2.39 TER points, respectively, with further notable improvements over the multilingual APE model observed through multi-task learning (+1.29 and +1.44 TER points), data augmentation (+0.53 and +0.45 TER points) and domain adaptation (+0.35 and +0.45 TER points). We release the synthetic data, code, and models accrued during this study publicly for further research.</abstract>
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%0 Conference Proceedings
%T Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages
%A Deoghare, Sourabh
%A Kanojia, Diptesh
%A Bhattacharyya, Pushpak
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F deoghare-etal-2024-together
%X This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages. Focusing on two closely related language pairs, English-Marathi and English-Hindi, we exploit the linguistic similarities to develop a robust multilingual APE model. To facilitate cross-linguistic transfer, we generate synthetic Hindi-Marathi and Marathi-Hindi APE triplets. Additionally, we incorporate a Quality Estimation (QE)-APE multi-task learning framework. While the experimental results underline the complementary nature of APE and QE, we also observe that QE-APE multitask learning facilitates effective domain adaptation. Our experiments demonstrate that the multilingual APE models outperform their corresponding English-Hindi and English-Marathi single-pair models by 2.5 and 2.39 TER points, respectively, with further notable improvements over the multilingual APE model observed through multi-task learning (+1.29 and +1.44 TER points), data augmentation (+0.53 and +0.45 TER points) and domain adaptation (+0.35 and +0.45 TER points). We release the synthetic data, code, and models accrued during this study publicly for further research.
%U https://aclanthology.org/2024.findings-emnlp.634
%P 10800-10812
Markdown (Informal)
[Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages](https://aclanthology.org/2024.findings-emnlp.634) (Deoghare et al., Findings 2024)
ACL