  While the progress of machine translation of written text has come far in the
past several years thanks to the increasing availability of parallel corpora
and corpora-based training technologies, automatic translation of spoken text
and dialogues remains challenging even for modern systems. In this paper, we
aim to boost the machine translation quality of conversational texts by
introducing a newly constructed Japanese-English business conversation parallel
corpus. A detailed analysis of the corpus is provided along with challenging
examples for automatic translation. We also experiment with adding the corpus
in a machine translation training scenario and show how the resulting system
benefits from its use.
