  Machine translation tools do not yet exist for the Yup'ik language, a
polysynthetic language spoken by around 8,000 people who live primarily in
Southwest Alaska. We compiled a parallel text corpus for Yup'ik and English and
developed a morphological parser for Yup'ik based on grammar rules. We trained
a seq2seq neural machine translation model with attention to translate Yup'ik
input into English. We then compared the influence of different tokenization
methods, namely rule-based, unsupervised (byte pair encoding), and unsupervised
morphological (Morfessor) parsing, on BLEU score accuracy for Yup'ik to English
translation. We find that using tokenized input increases the translation
accuracy compared to that of unparsed input. Although overall Morfessor did
best with a vocabulary size of 30k, our first experiments show that BPE
performed best with a reduced vocabulary size.
