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

• URL: http://arxiv.org/abs/2106.00143v1
• Date: Mon, 31 May 2021 23:21:10 GMT
• Title: An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers
• Authors: Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov
• Abstract summary: We show that multilingual, word-level 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.
• Score: 3.4355075318742165
• License: http://creativecommons.org/licenses/by/4.0/
• 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 these 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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