@inproceedings{maruf-etal-2018-contextual,
title = "Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations",
author = "Maruf, Sameen and
Martins, Andr{\'e} F. T. and
Haffari, Gholamreza",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6311",
doi = "10.18653/v1/W18-6311",
pages = "101--112",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="maruf-etal-2018-contextual">
<titleInfo>
<title>Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sameen</namePart>
<namePart type="family">Maruf</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">André</namePart>
<namePart type="given">F</namePart>
<namePart type="given">T</namePart>
<namePart type="family">Martins</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gholamreza</namePart>
<namePart type="family">Haffari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third Conference on Machine Translation: Research Papers</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Brussels, Belgium</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">maruf-etal-2018-contextual</identifier>
<identifier type="doi">10.18653/v1/W18-6311</identifier>
<location>
<url>https://aclanthology.org/W18-6311</url>
</location>
<part>
<date>2018-10</date>
<extent unit="page">
<start>101</start>
<end>112</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations
%A Maruf, Sameen
%A Martins, André F. T.
%A Haffari, Gholamreza
%S Proceedings of the Third Conference on Machine Translation: Research Papers
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F maruf-etal-2018-contextual
%X 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.
%R 10.18653/v1/W18-6311
%U https://aclanthology.org/W18-6311
%U https://doi.org/10.18653/v1/W18-6311
%P 101-112
Markdown (Informal)
[Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations](https://aclanthology.org/W18-6311) (Maruf et al., WMT 2018)
ACL