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
Organization: ByteDance Seed

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.