@inproceedings{steingrimsson-2023-ast,
title = "The {AST} Submission for the {C}o{C}o4{MT} 2023 Shared Task on Corpus Construction for Low-Resource Machine Translation",
author = "Steingr{\'i}msson, Stein{\th}{\'o}r",
booktitle = "Proceedings of the Second Workshop on Corpus Generation and Corpus Augmentation for Machine Translation",
month = sep,
year = "2023",
address = "Macau SAR, China",
publisher = "Asia-Pacific Association for Machine Translation",
url = "https://aclanthology.org/2023.mtsummit-coco4mt.5/",
pages = "33--38",
abstract = "We describe the AST submission for the CoCo4MT 2023 shared task. The aim of the task is to identify the best candidates for translation in a source data set with the aim to use the translated parallel data for fine-tuning the mBART-50 model. We experiment with three methods: scoring sentences based on n-gram coverage, using LaBSE to estimate semantic similarity and identify misalignments and mistranslations by comparing machine translated source sentences to corresponding manually translated segments in high-resource languages. We find that we obtain the best results by combining these three methods, using LaBSE and machine translation for filtering, and one of our n-gram scoring approaches for ordering sentences."
}
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<abstract>We describe the AST submission for the CoCo4MT 2023 shared task. The aim of the task is to identify the best candidates for translation in a source data set with the aim to use the translated parallel data for fine-tuning the mBART-50 model. We experiment with three methods: scoring sentences based on n-gram coverage, using LaBSE to estimate semantic similarity and identify misalignments and mistranslations by comparing machine translated source sentences to corresponding manually translated segments in high-resource languages. We find that we obtain the best results by combining these three methods, using LaBSE and machine translation for filtering, and one of our n-gram scoring approaches for ordering sentences.</abstract>
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%0 Conference Proceedings
%T The AST Submission for the CoCo4MT 2023 Shared Task on Corpus Construction for Low-Resource Machine Translation
%A Steingrímsson, Stein\thór
%S Proceedings of the Second Workshop on Corpus Generation and Corpus Augmentation for Machine Translation
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F steingrimsson-2023-ast
%X We describe the AST submission for the CoCo4MT 2023 shared task. The aim of the task is to identify the best candidates for translation in a source data set with the aim to use the translated parallel data for fine-tuning the mBART-50 model. We experiment with three methods: scoring sentences based on n-gram coverage, using LaBSE to estimate semantic similarity and identify misalignments and mistranslations by comparing machine translated source sentences to corresponding manually translated segments in high-resource languages. We find that we obtain the best results by combining these three methods, using LaBSE and machine translation for filtering, and one of our n-gram scoring approaches for ordering sentences.
%U https://aclanthology.org/2023.mtsummit-coco4mt.5/
%P 33-38
Markdown (Informal)
[The AST Submission for the CoCo4MT 2023 Shared Task on Corpus Construction for Low-Resource Machine Translation](https://aclanthology.org/2023.mtsummit-coco4mt.5/) (Steingrímsson, MTSummit 2023)
ACL