Character based Decoding in Tree to Sequence Attention based Neural Machine Translation
This paper reports our systems (UT AKY) submitted in the 3rd Workshop of Asian Translation 2016 (WAT'16) and their results in the English to Japanese translation task. Our model is based on the tree to sequence Attention based NMT (ANMT) model proposed by Eriguchi et al. (2016) We submitted two ANMT systems: one with a word based decoder and the other with a character based decoder. Experimenting on the English to Japanese translation task, we have confirmed that the character based decoder can cover almost the full vocabulary in the target language and generate translations much faster than the word based model.
WAT@COLING