Machine Translation Quality Estimation: Advances and Emerging Challenges

Dear Colleagues,

As machine translation (MT) systems continue to evolve and gain widespread use across various domains, the challenge of assessing translation quality without reference translations has become increasingly important. Quality estimation (QE) for machine translation allows the assessment of MT output in real-time, without human intervention. This is vital in many real-world applications, such as post-editing MT output, improving MT systems, or deploying MT systems, in contexts where reference translations or human assessors are scarce.

Despite considerable progress, numerous challenges persist in this expanding field, and new approaches are needed to improve QE models. As MT applications broaden to areas like healthcare, legal systems, education, and global communication, the demand for accurate, explainable, and robust quality estimation methods continues to grow. This demand is especially pronounced for low-resource languages, where MT systems face challenges due to scarcity of training data. Moreover, QE models are being increasingly integrated into MT workflows, where they not only guide systems toward improved translations but also aid in automated post-editing (APE) of detected errors and filter out low-quality translation candidates. Effectively utilising QE models in such integrated MT or APE approaches, particularly alongside large language models (LLMs), remains an open question. This Special Issue seeks to address both methodological innovations and practical applications, offering a comprehensive view of the current research and future directions in QE.

Topics of interest include, but are not limited to, the following:

• Novel architectures for QE of MT output;
• QE for low-resource languages and specialised domains;
• Integrating QE systems into MT and APE workflows/approaches;
• Transfer learning, domain adaptation, and multitask learning for QE;
• QE for specialised neural MT systems and large language models (LLMs);
• Integrating any type of external information to improve QE systems;
• Data efficiency: semi-supervised and unsupervised QE techniques;
• Building compact and computationally efficient QE models;
• Explainability and interpretability in QE models;
• Real-world applications in sectors such as healthcare, legal, education, and global communication;
• Benchmark datasets and resources for QE of MT output.

Dr. Arda Tezcan
Dr. Frédéric Blain
Dr. Miquel Esplà-Gomis
Guest Editors

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• machine translation
• quality estimation for MT
• low-resource languages
• explainability in AI
• large language models
• automatic post-editing
• machine learning