An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers

Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    Most studies on word-level Quality Estimation (QE) of machine translation focus on languagespecific 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 zeroshot 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.

    Original languageEnglish
    Title of host publicationProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
    PublisherAssociation for Computational Linguistics (ACL)
    Pages434-440
    Volume2
    ISBN (Electronic)9781954085527
    DOIs
    Publication statusPublished - Aug 2021
    EventJoint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021 - Virtual, Online
    Duration: 1 Aug 20216 Aug 2021
    https://2021.aclweb.org/

    Conference

    ConferenceJoint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021
    CityVirtual, Online
    Period1/08/216/08/21
    Internet address

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