  Advancements in unsupervised machine translation have enabled the development
of machine translation systems that can translate between languages for which
there is not an abundance of parallel data available. We explored unsupervised
machine translation between Mandarin Chinese and Cantonese. Despite the vast
number of native speakers of Cantonese, there is still no large-scale corpus
for the language, due to the fact that Cantonese is primarily used for oral
communication. The key contributions of our project include: 1. The creation of
a new corpus containing approximately 1 million Cantonese sentences, and 2. A
large-scale comparison across different model architectures, tokenization
schemes, and embedding structures. Our best model trained with character-based
tokenization and a Transformer architecture achieved a character-level BLEU of
25.1 when translating from Mandarin to Cantonese and of 24.4 when translating
from Cantonese to Mandarin. In this paper we discuss our research process,
experiments, and results.
