@inproceedings{ramesh-sankaranarayanan-2018-neural,
title = "Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora",
author = "Ramesh, Sree Harsha and
Sankaranarayanan, Krishna Prasad",
editor = "Cordeiro, Silvio Ricardo and
Oraby, Shereen and
Pavalanathan, Umashanthi and
Rim, Kyeongmin",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-4016",
doi = "10.18653/v1/N18-4016",
pages = "112--119",
abstract = "Resources for the non-English languages are scarce and this paper addresses this problem in the context of machine translation, by automatically extracting parallel sentence pairs from the multilingual articles available on the Internet. In this paper, we have used an end-to-end Siamese bidirectional recurrent neural network to generate parallel sentences from comparable multilingual articles in Wikipedia. Subsequently, we have showed that using the harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs: English{--}Hindi and English{--}Tamil, when compared to training exclusively on the limited bilingual corpora collected for these language pairs.",
}
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%0 Conference Proceedings
%T Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora
%A Ramesh, Sree Harsha
%A Sankaranarayanan, Krishna Prasad
%Y Cordeiro, Silvio Ricardo
%Y Oraby, Shereen
%Y Pavalanathan, Umashanthi
%Y Rim, Kyeongmin
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F ramesh-sankaranarayanan-2018-neural
%X Resources for the non-English languages are scarce and this paper addresses this problem in the context of machine translation, by automatically extracting parallel sentence pairs from the multilingual articles available on the Internet. In this paper, we have used an end-to-end Siamese bidirectional recurrent neural network to generate parallel sentences from comparable multilingual articles in Wikipedia. Subsequently, we have showed that using the harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs: English–Hindi and English–Tamil, when compared to training exclusively on the limited bilingual corpora collected for these language pairs.
%R 10.18653/v1/N18-4016
%U https://aclanthology.org/N18-4016
%U https://doi.org/10.18653/v1/N18-4016
%P 112-119
Markdown (Informal)
[Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora](https://aclanthology.org/N18-4016) (Ramesh & Sankaranarayanan, NAACL 2018)
ACL