  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
