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
this https URL along with a live demo at this https URL

使用自然语言处理工具需了解语言本身、熟悉最新模型和训练方法，并具备部署这些模型的技术能力。本文介绍了 CMU 语言注释后端，一个开源框架，简化了模型部署和连续的人机交互微调过程，使语言社区成员和语言学家能够使用自然语言处理工具。CMULAB 可以让用户快速适应和扩展现有的语音识别、OCR、翻译和句法分析工具，即使训练数据有限。我们描述了目前可用的各种工具和 API，以及开发人员如何轻松地将新模型 / 功能添加到该框架中。