Multilingual intent classification is central to customer-service systems on global logistics platforms, where models must process noisy user queries across languages and hierarchical label spaces. Yet most existing multilingual benchmarks rely on machine-translated text, which is typically cleaner and more standardized than native customer requests and can therefore overestimate real-world robustness. We present a public benchmark for hierarchical multilingual intent classification constructed from real logistics customer-service logs. The dataset contains approximately 30K de-identified, stand-alone user queries curated from 600K historical records through filtering, LLM-assisted quality control, and human verification, and is organized into a two-level taxonomy with 13 parent and 17 leaf intents. English, Spanish, and Arabic are included as seen languages, while Indonesian, Chinese, and additional test-only languages support zero-shot evaluation. To directly measure the gap between synthetic and real evaluation, we provide paired native and machine-translated test sets and benchmark multilingual encoders, embedding models, and small language models under flat and hierarchical protocols. Results show that translated test sets substantially overestimate performance on noisy native queries, especially for long-tail intents and cross-lingual transfer, underscoring the need for more realistic multilingual intent benchmarks.

翻译：多语言意图分类是全球物流平台客服系统的核心，模型需跨语言处理含噪用户查询及层次化标签空间。然而，现有的大多数多语言基准依赖于机器翻译文本，这类文本通常比原生客户请求更干净、更标准化，因此可能高估实际鲁棒性。我们提出了一个基于真实物流客服日志构建的层次化多语言意图分类公开基准。该数据集包含约30K条去标识化的独立用户查询，这些查询从600K条历史记录中通过过滤、LLM辅助质量控制和人工验证筛选而来，并按两级分类体系组织，包含13个父意图和17个子意图。英语、西班牙语和阿拉伯语作为已见语言，而印尼语、中文及其他仅用于测试的语言则支持零样本评估。为直接衡量合成评估与真实评估之间的差距，我们提供了配对的天然测试集与机器翻译测试集，并在扁平化和层次化协议下对多语言编码器、嵌入模型及小型语言模型进行了基准测试。结果表明，翻译测试集显著高估了模型在含噪原生查询上的性能，尤其是长尾意图和跨语言迁移场景，这凸显了构建更真实多语言意图基准的必要性。