@inproceedings{shapiro-duh-2019-comparing,
title = "Comparing Pipelined and Integrated Approaches to Dialectal {A}rabic Neural Machine Translation",
author = "Shapiro, Pamela and
Duh, Kevin",
editor = {Zampieri, Marcos and
Nakov, Preslav and
Malmasi, Shervin and
Ljube{\v{s}}i{\'c}, Nikola and
Tiedemann, J{\"o}rg and
Ali, Ahmed},
booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
month = jun,
year = "2019",
address = "Ann Arbor, Michigan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1424",
doi = "10.18653/v1/W19-1424",
pages = "214--222",
abstract = "When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.",
}
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<abstract>When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.</abstract>
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%0 Conference Proceedings
%T Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation
%A Shapiro, Pamela
%A Duh, Kevin
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Malmasi, Shervin
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Ali, Ahmed
%S Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2019
%8 June
%I Association for Computational Linguistics
%C Ann Arbor, Michigan
%F shapiro-duh-2019-comparing
%X When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.
%R 10.18653/v1/W19-1424
%U https://aclanthology.org/W19-1424
%U https://doi.org/10.18653/v1/W19-1424
%P 214-222
Markdown (Informal)
[Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation](https://aclanthology.org/W19-1424) (Shapiro & Duh, VarDial 2019)
ACL