  Recent works in neural machine translation have begun to explore document
translation. However, translating online multi-speaker conversations is still
an open problem. In this work, we propose the task of translating Bilingual
Multi-Speaker Conversations, and explore neural architectures which exploit
both source and target-side conversation histories for this task. To initiate
an evaluation for this task, we introduce datasets extracted from Europarl v7
and OpenSubtitles2016. Our experiments on four language-pairs confirm the
significance of leveraging conversation history, both in terms of BLEU and
manual evaluation.
