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We introduce ChrEnTranslate, an online machine translation demonstrationsystem for translation between English and an endangered language Cherokee. Itsupports both statistical and neural translation models as well as providesquality estimation to inform users of reliability, two user feedback interfacesfor experts and common users respectively, example inputs to collect humantranslations for monolingual data, word alignment visualization, and relevantterms from the Cherokee-English dictionary. The quantitative evaluationdemonstrates that our backbone translation models achieve state-of-the-arttranslation performance and our quality estimation well correlates with bothBLEU and human judgment. By analyzing 216 pieces of expert feedback, we findthat NMT is preferable because it copies less than SMT, and, in general,current models can translate fragments of the source sentence but make majormistakes. When we add these 216 expert-corrected parallel texts back into thetraining set and retrain models, equal or slightly better performance isobserved, which indicates the potential of human-in-the-loop learning. Ouronline demo is at https://chren.cs.unc.edu/ , our code is open-sourced athttps://github.com/ZhangShiyue/ChrEnTranslate , and our data is available athttps://github.com/ZhangShiyue/ChrEn

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