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

arXiv: https://arxiv.org/abs/2404.02408 · HTML: https://mlsanity.com/paper/2404.02408/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Rapid Adaptation of Neural Machine Translation to New Languages](https://mlsanity.com/paper/1808.04189/) (2018)
- [Universal Phone Recognition with a Multilingual Allophone System](https://mlsanity.com/paper/2002.11800/) (2020)
- [Prompt2Model: Generating Deployable Models from Natural Language Instructions](https://mlsanity.com/paper/2308.12261/) (2023)
- [Exploiting Adapters for Cross-lingual Low-resource Speech Recognition](https://mlsanity.com/paper/2105.11905/) (2021)
- [OCR Post Correction for Endangered Language Texts](https://mlsanity.com/paper/2011.05402/) (2020)
- [Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation](https://mlsanity.com/paper/2305.10951/) (2023)
- [GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text](https://mlsanity.com/paper/2403.06399/) (2024)
- [Towards a General-Purpose Linguistic Annotation Backend](https://mlsanity.com/paper/1812.05272/) (2018)

## Built on by (most cited first, 1 of 1)

- [Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing](https://mlsanity.com/paper/2406.11085/) (2024)

## 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