Large language models (LLMs) have made significant strides in various natural language
processing (NLP) tasks. Recent research shows that the moderately-sized LLMs often …

Context-aware machine translation models are designed to leverage contextual information,
but often fail to do so. As a result, they inaccurately disambiguate pronouns and polysemous …

Despite the known limitations, most machine translation systems today still operate on the
sentence-level. One reason for this is, that most parallel training data is only sentence-level …

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 …

This paper addresses the task of contextual translation using multi-segment models.
Specifically we show that increasing model capacity further pushes the limits of this …

Document-level contextual information has shown benefits to text-based machine
translation, but whether and how context helps end-to-end (E2E) speech translation (ST) is …

Multi-encoder models are a broad family of context-aware neural machine translation
systems that aim to improve translation quality by encoding document-level contextual …

Document-level neural machine translation (DocNMT) achieves coherent translations by
incorporating cross-sentence context. However, for most language pairs there's a shortage …

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

Context-aware neural machine translation (NMT) incorporates contextual information of
surrounding texts, that can improve the translation quality of document-level machine …