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.

本文探讨了以往机器翻译的词汇质量评估模型的局限性，并提出了基于强大的预训练Transformer模型的跨语言通用性的词汇质量评估模型，证明其在跨语言模型训练、零样本/少样本数据归集的情况下，很好地泛化了，并且在实际应用中具有更广泛的应用前景。

跨语言Transformer多语言词级质量评估的探索性分析