  Cherokee is a highly endangered Native American language spoken by the
Cherokee people. The Cherokee culture is deeply embedded in its language.
However, there are approximately only 2,000 fluent first language Cherokee
speakers remaining in the world, and the number is declining every year. To
help save this endangered language, we introduce ChrEn, a Cherokee-English
parallel dataset, to facilitate machine translation research between Cherokee
and English. Compared to some popular machine translation language pairs, ChrEn
is extremely low-resource, only containing 14k sentence pairs in total. We
split our parallel data in ways that facilitate both in-domain and
out-of-domain evaluation. We also collect 5k Cherokee monolingual data to
enable semi-supervised learning. Besides these datasets, we propose several
Cherokee-English and English-Cherokee machine translation systems. We compare
SMT (phrase-based) versus NMT (RNN-based and Transformer-based) systems;
supervised versus semi-supervised (via language model, back-translation, and
BERT/Multilingual-BERT) methods; as well as transfer learning versus
multilingual joint training with 4 other languages. Our best results are
15.8/12.7 BLEU for in-domain and 6.5/5.0 BLEU for out-of-domain Chr-En/EnChr
translations, respectively, and we hope that our dataset and systems will
encourage future work by the community for Cherokee language revitalization.
Our data, code, and demo will be publicly available at
https://github.com/ZhangShiyue/ChrEn
