  For different language pairs, word-level neural machine translation (NMT)
models with a fixed-size vocabulary suffer from the same problem of
representing out-of-vocabulary (OOV) words. The common practice usually
replaces all these rare or unknown words with a <UNK> token, which limits the
translation performance to some extent. Most of recent work handled such a
problem by splitting words into characters or other specially extracted subword
units to enable open-vocabulary translation. Byte pair encoding (BPE) is one of
the successful attempts that has been shown extremely competitive by providing
effective subword segmentation for NMT systems. In this paper, we extend the
BPE style segmentation to a general unsupervised framework with three
statistical measures: frequency (FRQ), accessor variety (AV) and description
length gain (DLG). We test our approach on two translation tasks: German to
English and Chinese to English. The experimental results show that AV and DLG
enhanced systems outperform the FRQ baseline in the frequency weighted schemes
at different significant levels.
