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Found 3 papers, 1 papers with code
MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation
This work introduces MTCue, a novel neural machine translation (NMT) framework that interprets all context (including discrete variables) as text.
Reference-less Analysis of Context Specificity in Translation with Personalised Language Models
Our results suggest that the degree to which professional translations in our domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model, which is also reflected in the contextual model's superior reference-based scores.
Towards Personalised and Document-level Machine Translation of Dialogue
State-of-the-art (SOTA) neural machine translation (NMT) systems translate texts at sentence level, ignoring context: intra-textual information, like the previous sentence, and extra-textual information, like the gender of the speaker.
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