# ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback

arXiv: https://arxiv.org/abs/2107.14800 · HTML: https://mlsanity.com/paper/2107.14800/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Unsupervised Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2005.10608/) (2020)
- [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://mlsanity.com/paper/2010.04791/) (2020)

## Built on by (most cited first, 2 of 2)

- [How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language](https://mlsanity.com/paper/2204.11909/) (2022)
- [Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://mlsanity.com/paper/2306.06804/) (2023)

## Abstract (arXiv)

We introduce ChrEnTranslate, an online machine translation demonstration system for translation between English and an endangered language Cherokee. It supports both statistical and neural translation models as well as provides quality estimation to inform users of reliability, two user feedback interfaces for experts and common users respectively, example inputs to collect human translations for monolingual data, word alignment visualization, and relevant terms from the Cherokee-English dictionary. The quantitative evaluation demonstrates that our backbone translation models achieve state-of-the-art translation performance and our quality estimation well correlates with both BLEU and human judgment. By analyzing 216 pieces of expert feedback, we find that NMT is preferable because it copies less than SMT, and, in general, current models can translate fragments of the source sentence but make major mistakes. When we add these 216 expert-corrected parallel texts back into the training set and retrain models, equal or slightly better performance is observed, which indicates the potential of human-in-the-loop learning. Our online demo is at https://chren.cs.unc.edu/ , our code is open-sourced at https://github.com/ZhangShiyue/ChrEnTranslate , and our data is available at https://github.com/ZhangShiyue/ChrEn