Enhanced Simultaneous Machine Translation with Word-level Policies
Kang Kim Hankyu Cho
Recent years have seen remarkable advances in the field of Simultaneous
Machine Translation (SiMT) due to the introduction of innovative policies that
dictate whether to READ or WRITE at each step of the translation process.
However, a common assumption in many existing studies is that operations are
carried out at the subword level, even though the standard unit for input and
output in most practical scenarios is typically at the word level. This paper
demonstrates that policies devised and validated at the subword level are
surpassed by those operating at the word level, which process multiple subwords
to form a complete word in a single step. Additionally, we suggest a method to
boost SiMT models using language models (LMs), wherein the proposed word-level
policy plays a vital role in addressing the subword disparity between LMs and
SiMT models. Code is available at https://github.com/xl8-ai/WordSiMT.