Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations

Sameen Maruf, André F. T. Martins, Gholamreza Haffari


Abstract
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.
Anthology ID:
W18-6311
Volume:
Proceedings of the Third Conference on Machine Translation: Research Papers
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–112
Language:
URL:
https://aclanthology.org/W18-6311
DOI:
10.18653/v1/W18-6311
Bibkey:
Cite (ACL):
Sameen Maruf, André F. T. Martins, and Gholamreza Haffari. 2018. Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 101–112, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations (Maruf et al., 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/W18-6311.pdf
Code
 sameenmaruf/Bi-MSMT
Data
OpenSubtitles