DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains
Authors: name: Xiying Zhao; name: Zhoufutu Wen; name: Zhixuan Chen; name: Jingzhe Ding; name: Jianpeng Jiao; name: Shuai Li; name: Xi Li; name: Danni Liang; name: Shengda Long; name: Qianqian Liu; name: Xianbo Wu; name: Hongwan Gao; name: Xiang Gao; name: Liang Hu; name: Jiashuo Liu; name: Mengyun Liu; name: Weiran Shi; name: Chenghao Yang; name: Qianyu Yang; name: Xuanliang Zhang; name: Ge Zhang; name: Wenhao Huang
Published: 2025-11-14 06:09:37

Abstract: The evaluation of discourse-level translation in expert domains remains inadequate, despite its centrality to knowledge dissemination and cross-lingual scholarly communication. While these translations demand discourse-level coherence and strict terminological precision, current evaluation methods predominantly focus on segment-level accuracy and fluency. To address this limitation, we introduce DiscoX, a new benchmark for discourse-level and expert-level Chinese-English translation. It comprises 200 professionally-curated texts from 7 domains, with an average length exceeding 1700 tokens. To evaluate performance on DiscoX, we also develop Metric-S, a reference-free system that provides fine-grained automatic assessments across accuracy, fluency, and appropriateness. Metric-S demonstrates strong consistency with human judgments, significantly outperforming existing metrics. Our experiments reveal a remarkable performance gap: even the most advanced LLMs still trail human experts on these tasks. This finding validates the difficulty of DiscoX and underscores the challenges that remain in achieving professional-grade machine translation. The proposed benchmark and evaluation system provide a robust framework for more rigorous evaluation, facilitating future advancements in LLM-based translation.