An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers
Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov
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.
BibTeX
@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"
}