TransBench: Benchmarking Machine Translation for Industrial-Scale Applications
Haijun Li, Tianqi Shi, Zifu Shang, Yuxuan Han, Xueyu Zhao, Hao Wang, Yu Qian, Zhiqiang Qian, Linlong Xu, Minghao Wu, Chenyang Lyu, Longyue Wang, Gongbo Tang, Weihua Luo, Zhao Xu, Kaifu Zhang

TL;DR
This paper introduces TransBench, a comprehensive benchmark for evaluating industrial machine translation systems, focusing on domain-specific, cultural, and stylistic aspects crucial for real-world applications.
Contribution
It presents a new multi-level evaluation framework, a publicly available benchmark for e-commerce translation, and novel metrics for assessing industry-specific translation quality.
Findings
TransBench covers 33 language pairs and 4 scenarios with 17,000 sentences.
Traditional metrics combined with Marco-MOS improve evaluation accuracy.
Open-source tools facilitate reproducible and systematic assessment.
Abstract
Machine translation (MT) has become indispensable for cross-border communication in globalized industries like e-commerce, finance, and legal services, with recent advancements in large language models (LLMs) significantly enhancing translation quality. However, applying general-purpose MT models to industrial scenarios reveals critical limitations due to domain-specific terminology, cultural nuances, and stylistic conventions absent in generic benchmarks. Existing evaluation frameworks inadequately assess performance in specialized contexts, creating a gap between academic benchmarks and real-world efficacy. To address this, we propose a three-level translation capability framework: (1) Basic Linguistic Competence, (2) Domain-Specific Proficiency, and (3) Cultural Adaptation, emphasizing the need for holistic evaluation across these dimensions. We introduce TransBench, a benchmark…
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Taxonomy
TopicsNatural Language Processing Techniques
