Artificial intelligence has advanced significantly in recent years, affecting multiple aspects of
life. In particular, this has had an impact on the machine translation of texts, reducing or …

Establishing whether language models can use contextual information in a human-plausible
way is important to ensure their trustworthiness in real-world settings. However, the …

As generic machine translation (MT) quality has improved, the need for targeted
benchmarks that explore fine-grained aspects of quality has increased. In particular, gender …

Context-aware translation can be achieved by processing a concatenation of consecutive
sentences with the standard Transformer architecture. This paper investigates the intuitive …

Document-level context for neural machine translation (NMT) is crucial to improve the
translation consistency and cohesion, the translation of ambiguous inputs, as well as several …

Context-aware Machine Translation aims to improve translations of sentences by
incorporating surrounding sentences as context. Towards this task, two main architectures …

Compared to sentence-level systems, document-level neural machine translation (NMT)
models produce a more consistent output across a document and are able to better resolve …

Abstract Document-level Machine Translation has emerged as a promising means to
enhance automated translation quality, but it is currently unclear how effectively context …

In this paper, we investigate the role of attention heads in Context-aware Machine
Translation models for pronoun disambiguation in the English-to-German and English-to …

We explore the use of source factors in context-aware neural machine translation,
specifically concatenation-based models, to improve the translation quality of inter-sentential …