Contextual neural model for translating bilingual multi-speaker conversations

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationWMT 2018 - Third Conference on Machine Translation - Proceedings of the Conference
Subtitle of host publicationOctober 31 - November 1, 2018 Brussels, Belgium
EditorsOndˇrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor, Mark Fishel
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages12
ISBN (Electronic)9781948087810
Publication statusPublished - 2018
EventEmpirical Methods in Natural Language Processing 2018 - Brussels, Belgium
Duration: 31 Oct 20184 Nov 2018 (Proceedings)


ConferenceEmpirical Methods in Natural Language Processing 2018
Abbreviated titleEMNLP 2018
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