mlsanityData to 7 Oct 2026

CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models

arXiv 2404.02408 ↗First posted 3 Apr 2024cs.CLcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 21 references that are in the map. First posted on arXiv 3 Apr 2024.

Builds on

15 of 21 references.

References · 15

  1. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
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3 more
  1. 2023Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentationcited by 30
  2. 2024GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Textcited by 12
  3. 2018Towards a General-Purpose Linguistic Annotation Backendcited by 1

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossingcited by 5