G^2C-MT: Graph-Guided Context Selection for Document-Level Machine Translation

In three linesG²C-MT proposes graph-guided context selection for document-level machine translation. The system models discourse dependencies between paragraphs via a lightweight graph and uses depth-biased random walks to extract context paths. Tested on DeepSeek-V3, Gemini-2.5-Flash-lite, and Qwen-2.5/3, the approach outperforms baselines across multiple domains.

Summary generated by Claude — human-verified