Published on May, 11 2021 by RGCL.
Title: An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers.
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 case of zero-shot QE, we show 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 indicate that the word level QE models based on powerful pre-trained transformers we propose on this paper generalise well across languages, making them more useful in real-world scenarios.