  In neural machine translation (NMT), researchers face the challenge of
un-seen (or out-of-vocabulary OOV) words translation. To solve this, some
researchers propose the splitting of western languages such as English and
German into sub-words or compounds. In this paper, we try to address this OOV
issue and improve the NMT adequacy with a harder language Chinese whose
characters are even more sophisticated in composition. We integrate the Chinese
radicals into the NMT model with different settings to address the unseen words
challenge in Chinese to English translation. On the other hand, this also can
be considered as semantic part of the MT system since the Chinese radicals
usually carry the essential meaning of the words they are constructed in.
Meaningful radicals and new characters can be integrated into the NMT systems
with our models. We use an attention-based NMT system as a strong baseline
system. The experiments on standard Chinese-to-English NIST translation shared
task data 2006 and 2008 show that our designed models outperform the baseline
model in a wide range of state-of-the-art evaluation metrics including LEPOR,
BEER, and CharacTER, in addition to BLEU and NIST scores, especially on the
adequacy-level translation. We also have some interesting findings from the
results of our various experiment settings about the performance of words and
characters in Chinese NMT, which is different with other languages. For
instance, the fully character level NMT may perform well or the state of the
art in some other languages as researchers demonstrated recently, however, in
the Chinese NMT model, word boundary knowledge is important for the model
learning.
