@inproceedings{ranasinghe-etal-2021-exploratory,
title = "An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers",
author = "Ranasinghe, Tharindu and
Orasan, Constantin and
Mitkov, Ruslan",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.55",
doi = "10.18653/v1/2021.acl-short.55",
pages = "434--440",
abstract = "Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, word-level QE. We show that multilingual QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs. Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.",
}
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<abstract>Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, word-level QE. We show that multilingual QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs. Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.</abstract>
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%0 Conference Proceedings
%T An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers
%A Ranasinghe, Tharindu
%A Orasan, Constantin
%A Mitkov, Ruslan
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F ranasinghe-etal-2021-exploratory
%X Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, word-level QE. We show that multilingual QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs. Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.
%R 10.18653/v1/2021.acl-short.55
%U https://aclanthology.org/2021.acl-short.55
%U https://doi.org/10.18653/v1/2021.acl-short.55
%P 434-440
Markdown (Informal)
[An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers](https://aclanthology.org/2021.acl-short.55) (Ranasinghe et al., ACL-IJCNLP 2021)
ACL