arXiv:2606.03078v1 Announce Type: new Abstract: Effective document-level machine translation (DocMT) requires capturing long-range discourse dependencies. Recent work has explored retrieval-based and discourse-aware context selection. However, these approaches often lack an explicit mechanism for modeling structured discourse dependencies between distant paragraphs in a document. In this paper, we propose G^2C-MT (Graph-Guided Context for Machine Translation), which views DocMT context selection as a structured path discovery problem on a lightweight discourse graph, rather than retrieving uns
The continuous advancements in AI research, particularly in natural language processing, are pushing the boundaries of machine translation to address complex linguistic challenges like long-range discourse dependencies.
Improving document-level machine translation quality enhances cross-linguistic communication and information exchange, directly impacting global business, diplomacy, and scientific collaboration.
This research introduces a novel, graph-guided approach to context selection in DocMT, potentially leading to more coherent and accurate translations of extended texts.
- · Machine translation researchers
- · Global businesses relying on multilingual content
- · International organizations
- · Users of translation services
- · Previous less-effective DocMT methods
- · Translators specializing in highly nuanced, long-form content (potential displac
Immediate improvement in the quality of machine-translated documents across various domains.
Increased reliance on automated translation for complex documents, reducing the need for human post-editing in certain contexts.
Accelerated dissemination of research and information across language barriers, potentially fostering more rapid global innovation and understanding.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL