  In this paper, a novel approach is proposed to automatically construct
parallel discourse corpus for dialogue machine translation. Firstly, the
parallel subtitle data and its corresponding monolingual movie script data are
crawled and collected from Internet. Then tags such as speaker and discourse
boundary from the script data are projected to its subtitle data via an
information retrieval approach in order to map monolingual discourse to
bilingual texts. We not only evaluate the mapping results, but also integrate
speaker information into the translation. Experiments show our proposed method
can achieve 81.79% and 98.64% accuracy on speaker and dialogue boundary
annotation, and speaker-based language model adaptation can obtain around 0.5
BLEU points improvement in translation qualities. Finally, we publicly release
around 100K parallel discourse data with manual speaker and dialogue boundary
annotation.
