Deep Learning has been successfully applied to many areas including Natural Language Processing, Speech Recognition and Image Processing. Deep learning techniques have surprised the entire community both academy and industry by powerfully learning from data.
Recently, deep learning has been introduced to Machine Translation (MT). It first started as a kind of feature which was integrated in standard phrase or syntax-based statistical approaches. Deep learning has been shown useful in translation and language modeling as well as in reordering, tuning and rescoring. Additionally, deep learning has been applied to MT evaluation and quality estimation.
But the biggest impact on MT appeared with the new paradigm proposal: Neural MT, which has just recently (in the Workshop of Machine Translation 2015) outperformed state-of-the-art systems. This new approach uses an autoencoder architecture to build a neural system that is capable of translating. With the new approach, the new big MT challenges lie on how to deal with large vocabularies, document translation and computational power among others*.*
This hot topic is raising interest from the scientific community and as a response there have been several related events (i.e. tutorial <#m_-2405071807023874716_m_8694670399740380760_-728715324_-1975383620_-1177276990_1928584617_684013530__ftn1> and winter school <#m_-2405071807023874716_m_8694670399740380760_-728715324_-1975383620_-1177276990_1928584617_684013530__ftn2>). Moreover, the number of publications on this topic in top conferences such as ACL, NAACL, EMNLP has dramatically increased in the last three years. This would be the first special issue related to the topic. With this special issue, we pretend to offer a compilation of works that give the reader a global vision of how the deep learning techniques are applied to MT and what new challenges offers.
This Special Issue expects high quality submissions on the following topics (but not limited):
· Including deep learning knowledge in standard MT approaches (statistical, rule-based, example-based...)
· Neural MT approaches
· MT hybrid techniques using deep learning
· Deep learning challenges in MT: vocabulary limitation, document translation, computational power
· MT evaluation with deep learning techniques
· MT quality estimation with deep learning techniques
· Using deep learning in spoken language translation
Submission deadline [EXTENDED]: *18th April 2016 *
Notification of rejection/re-submission: 30th July 2016
Notification of final acceptance: 30th October 2016
Expected publication date: 30th January 2017
Marta R. Costa-jussà, Universitat Politècnica de Catalunya, Spain. marta.ruiz at upc.edu
Alexandre Allauzen, Centre National de la Recherche Scientifique, France. allauzen at limsi.fr
Loïc Barrault, Université du Maine, France. loic.barrault at lium.univ-lemans.fr
Kyunghyun Cho, New York University, USA. kyunghyun.cho at nyu.edu
Holger Schwenk, Facebook, USA. schwenk at fb.com
 <#m_-2405071807023874716_m_8694670399740380760_-728715324_-1975383620_-1177276990_1928584617_684013530__ftnref1> http://naacl.org/naacl-hlt-2015/tutorial-deep-learning.html
 <#m_-2405071807023874716_m_8694670399740380760_-728715324_-1975383620_-1177276990_1928584617_684013530__ftnref2> http://dl4mt.computing.dcu.ie/
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