Effectively using Natural Language Processing (NLP) tools in under-resourced languages requires a thorough understanding of the language itself, familiarity with the latest models and training methodologies, and technical expertise to deploy these models. This could present a significant obstacle for language community members and linguists to use NLP tools. This paper introduces the CMU Linguistic Annotation Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages, even with limited training data. We describe various tools and APIs that are currently available and how developers can easily add new models/functionality to the framework. Code is available at https://github.com/neulab/cmulab along with a live demo at https://cmulab.dev

翻译：在资源匮乏的语言中有效使用自然语言处理（NLP）工具，要求使用者对语言本身有深入理解、熟悉最新模型与训练方法，并具备部署这些模型的技术能力。这对于语言社区成员和语言学家使用NLP工具可能构成重大障碍。本文介绍CMU语言标注后端（CMU Linguistic Annotation Backend），这是一个简化模型部署与持续人机协同微调的开源框架。CMULAB使用户能够利用多语言模型的能力，快速将语音识别、光学字符识别（OCR）、翻译及句法分析等现有工具适配并扩展到新语言，即使训练数据有限。我们描述了当前可用的多种工具和API，并说明了开发者如何便捷地向框架中添加新模型或功能。代码及在线演示分别托管于 https://github.com/neulab/cmulab 和 https://cmulab.dev。