通过翻译流水线和压缩语言模型实现高效的多语言对话处理
Efficient Multilingual Dialogue Processing via Translation Pipelines and Distilled Language Models
AI总结：
本文提出通过翻译流水线和压缩语言模型实现多语言对话处理，展示在低资源语言上无需微调即可取得高性能的成果。
摘要：
本文介绍了团队Kl33n3x为NLPAI4Health 2025共享任务开发的多语言对话摘要和问答系统。该方法采用三阶段流程：将印度语言翻译成英语，使用一个参数为2.55B的压缩语言模型进行多任务文本生成，以及将文本翻译回源语言。通过利用知识蒸馏技术，本工作证明了紧凑模型在九种语言上都能取得高度竞争性的性能。该系统在竞赛任务中表现强劲，尤其在马拉雅拉姆语（86.7% QnA）、泰米尔语（86.7% QnA）和印地语（80.0% QnA）上表现尤为稳健，展示了基于翻译的方法在低资源语言处理中无需特定任务微调的有效性。
英文摘要：
This paper presents team Kl33n3x's multilingual dialogue summarization and question answering system developed for the NLPAI4Health 2025 shared task. The approach employs a three-stage pipeline: forward translation from Indic languages to English, multitask text generation using a 2.55B parameter distilled language model, and reverse translation back to source languages. By leveraging knowledge distillation techniques, this work demonstrates that compact models can achieve highly competitive performance across nine languages. The system achieved strong win rates across the competition's tasks, with particularly robust performance on Marathi (86.7% QnA), Tamil (86.7% QnA), and Hindi (80.0% QnA), demonstrating the effectiveness of translation-based approaches for low-resource language processing without task-specific fine-tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
