  This year, the Nara Institute of Science and Technology (NAIST)'s submission
to the 2015 Workshop on Asian Translation was based on syntax-based statistical
machine translation, with the addition of a reranking component using neural
attentional machine translation models. Experiments re-confirmed results from
previous work stating that neural MT reranking provides a large gain in
objective evaluation measures such as BLEU, and also confirmed for the first
time that these results also carry over to manual evaluation. We further
perform a detailed analysis of reasons for this increase, finding that the main
contributions of the neural models lie in improvement of the grammatical
correctness of the output, as opposed to improvements in lexical choice of
content words.
