Adapting large language models to full document translation remains challenging due to the difficulty of capturing long-range dependencies and preserving discourse coherence throughout extended texts. While recent agentic machine translation systems mitigate context window constraints through multi-agent orchestration and persistent memory, they require substantial computational resources and are sensitive to memory retrieval strategies. We introduce TransGraph, a discourse-guided framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than relying on sequential or exhaustive context. Across three document-level MT benchmarks spanning six languages and diverse domains, TransGraph consistently surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead.
@article{arxiv.2511.07230,
title = {Discourse Graph Guided Document Translation with Large Language Models},
author = {Viet-Thanh Pham and Minghan Wang and Hao-Han Liao and Thuy-Trang Vu},
journal= {arXiv preprint arXiv:2511.07230},
year = {2025}
}