面向多语言拼写纠正的多教师蒸馏
Multi-teacher Distillation for Multilingual Spelling Correction
AI总结：
本文提出多教师蒸馏方法，为每种语言训练单语言教师模型并蒸馏为单一多语言学生模型，实验证明其能高效满足全球搜索服务的多语言拼写纠正与严格延迟要求。
摘要：
准确的拼写纠正是现代搜索界面中的关键步骤，特别是在移动设备和语音转文本界面的时代。对于在全球范围部署的服务而言，这给多语言NLP带来了重大挑战：需要在所有语言中捕获并纠正拼写错误，甚至在使用多种语言的查询中也是如此。在本文中，我们使用多教师蒸馏来解决这一挑战。在我们的方法中，为每种语言/区域设置训练一个单语言教师模型，并将这些独立模型蒸馏成一个旨在服务所有语言/区域设置的单一多语言学生模型。在使用开源数据以及来自全球搜索服务的用户数据的实验中，我们表明这产生了高效的拼写纠正模型，能够满足已部署服务的严格延迟要求。
英文摘要：
Accurate spelling correction is a critical step in modern search interfaces, especially in an era of mobile devices and speech-to-text interfaces. For services that are deployed around the world, this poses a significant challenge for multilingual NLP: spelling errors need to be caught and corrected in all languages, and even in queries that use multiple languages. In this paper, we tackle this challenge using multi-teacher distillation. On our approach, a monolingual teacher model is trained for each language/locale, and these individual models are distilled into a single multilingual student model intended to serve all languages/locales. In experiments using open-source data as well as user data from a worldwide search service, we show that this leads to highly effective spelling correction models that can meet the tight latency requirements of deployed services.
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。
