@inproceedings{sen-etal-2016-iitp,
title = "{IITP} {E}nglish-{H}indi Machine Translation System at {WAT} 2016",
author = "Sen, Sukanta and
Banik, Debajyoty and
Ekbal, Asif and
Bhattacharyya, Pushpak",
editor = "Nakazawa, Toshiaki and
Mino, Hideya and
Ding, Chenchen and
Goto, Isao and
Neubig, Graham and
Kurohashi, Sadao and
Riza, Ir. Hammam and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4622",
pages = "216--222",
abstract = "In this paper we describe the system that we develop as part of our participation in WAT 2016. We develop a system based on hierarchical phrase-based SMT for English to Hindi language pair. We perform re-ordering and augment bilingual dictionary to improve the performance. As a baseline we use a phrase-based SMT model. The MT models are fine-tuned on the development set, and the best configurations are used to report the evaluation on the test set. Experiments show the BLEU of 13.71 on the benchmark test data. This is better compared to the official baseline BLEU score of 10.79.",
}
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%0 Conference Proceedings
%T IITP English-Hindi Machine Translation System at WAT 2016
%A Sen, Sukanta
%A Banik, Debajyoty
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Nakazawa, Toshiaki
%Y Mino, Hideya
%Y Ding, Chenchen
%Y Goto, Isao
%Y Neubig, Graham
%Y Kurohashi, Sadao
%Y Riza, Ir. Hammam
%Y Bhattacharyya, Pushpak
%S Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F sen-etal-2016-iitp
%X In this paper we describe the system that we develop as part of our participation in WAT 2016. We develop a system based on hierarchical phrase-based SMT for English to Hindi language pair. We perform re-ordering and augment bilingual dictionary to improve the performance. As a baseline we use a phrase-based SMT model. The MT models are fine-tuned on the development set, and the best configurations are used to report the evaluation on the test set. Experiments show the BLEU of 13.71 on the benchmark test data. This is better compared to the official baseline BLEU score of 10.79.
%U https://aclanthology.org/W16-4622
%P 216-222
Markdown (Informal)
[IITP English-Hindi Machine Translation System at WAT 2016](https://aclanthology.org/W16-4622) (Sen et al., WAT 2016)
ACL