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.
@article{arxiv.1809.00344,
title = {Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations},
author = {Sameen Maruf and André F. T. Martins and Gholamreza Haffari},
journal= {arXiv preprint arXiv:1809.00344},
year = {2018}
}