---
格式版本: 2
标题: "A Cost-Efficient Routing Pipeline for Multilingual Short-Text Classification Using Small Language Models"
原文链接: "https://arxiv.org/abs/2608.10939"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "rule:local:strict_original_body"
发布时间证据: "**\\[v1\\]** Tue, 11 Aug 2026 14:06:53 UTC (75 KB)"
发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body"
发布时间校准置信度: "high"
发布时间候选数量: 18
发布时间严格候选数量: 6
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T11:22:25+08:00"
发布时间仲裁状态: "skipped"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T11:20:34+08:00"
入库时间: "2026-08-12T03:22:25.221Z"
来源平台: "arXiv 学术论文搜索"
搜索渠道: "source_template"
搜索词: "https://arxiv.org/search/?query=latency&searchtype=all"
匹配关键词:
  - "latency"
  - "AI"
相关厂家:
  []
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 5
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "该论文为多语言短文本分类的NLP研究，与超节点、AI Rack、机柜级AI基础设施、供电、散热、高速互连或量产落地等项目主题完全无关，主题相关性0分。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T11:22:36+08:00"
AI主题相关性: 0
AI来源权威性: 2
AI新颖性: 1
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 2
采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.10939"
---

## Computer Science > Computation and Language

## Title:A Cost-Efficient Routing Pipeline for Multilingual Short-Text Classification Using Small Language Models

Authors:[Wajdi Ben Saad](https://arxiv.org/search/cs?searchtype=author&query=Saad,+W+B), [Safa Madiouni](https://arxiv.org/search/cs?searchtype=author&query=Madiouni,+S)

[View PDF](https://arxiv.org/pdf/2608.10939) [HTML (experimental)](https://arxiv.org/html/2608.10939v1)

> Abstract:Multilingual short-text classification supports operational systems such as content moderation, customer support routing, and intent recognition, yet aggregate evaluation often hides large differences between high-resource and low-resource languages. Uniform inference policies are simple to deploy, but they assume that all languages are equally well served. In this work, we evaluate a fixed-list routing strategy that keeps stronger languages on a direct multilingual path and selectively sends weaker languages through translation into English before zero-shot classification. The pipeline is fully self-hosted, uses pretrained compact sentence encoders, and requires no task-specific fine-tuning.  
> We test the approach on two benchmarks chosen to differ in scale and label granularity: a 15-language subset of SIB-200 for seven-way topic classification and a 15-locale subset of MASSIVE for intent classification over an official 60-intent inventory. On SIB-200, the best overall configuration is R1, which translates only the low-resource tier: high-tier and mid-tier Macro-F1 remain unchanged, while low-tier Macro-F1 rises from 0.4632 to 0.6828. On the MASSIVE subset, the same low-tier intervention raises low-tier Macro-F1 from 0.2143 to 0.4417, but the best overall result is obtained by full translation, R3, at Macro-F1 0.4647. Across these two benchmarks, selective translation is a reliable intervention for weaker languages, whereas the optimal routing boundary depends on the task. We therefore report routing through tier-level quality gains and tier-level latency rather than a single global efficiency score.

| Comments: |  |
| --- | --- |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | [arXiv:2608.10939](https://arxiv.org/abs/2608.10939) \[cs.CL\] |
|  | (or [arXiv:2608.10939v1](https://arxiv.org/abs/2608.10939v1) \[cs.CL\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10939](https://doi.org/10.48550/arXiv.2608.10939) |

## Submission history

From: Wajdi Ben Saad \[[view email](https://arxiv.org/show-email/a515df1d/2608.10939)\]  
**\[v1\]** Tue, 11 Aug 2026 14:06:53 UTC (75 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10939) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
