@inproceedings{a6fa426b9a64458aad3d9615cd202ac3,
title = "Overcoming the curse of sentence length for neural machine translation using automatic segmentation",
abstract = "The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences.",
author = "Jean Pouget-Abadie and Dzmitry Bahdanau and {van Merri{\"e}nboer}, Bart and Kyunghyun Cho and Yoshua Bengio",
note = "Funding Information: The authors would like to acknowledge the support of the following agencies for research funding and computing support: NSERC, Calcul Qu{\'e}bec, Compute Canada, the Canada Research Chairs and CIFAR. Publisher Copyright: {\textcopyright} 2014 Association for Computational Linguistics; 8th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2014 ; Conference date: 25-10-2014",
year = "2014",
language = "English (US)",
series = "Proceedings of SSST 2014 - 8th Workshop on Syntax, Semantics and Structure in Statistical Translation",
publisher = "Association for Computational Linguistics (ACL)",
pages = "78--85",
editor = "Dekai Wu and Marine Carpuat and Xavier Carreras and Vecchi, {Eva Maria}",
booktitle = "Proceedings of SSST 2014 - 8th Workshop on Syntax, Semantics and Structure in Statistical Translation",
}