@inproceedings{wang-etal-2017-hybrid,
title = "Hybrid Neural Network Alignment and Lexicon Model in Direct {HMM} for Statistical Machine Translation",
author = "Wang, Weiyue and
Alkhouli, Tamer and
Zhu, Derui and
Ney, Hermann",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2020",
doi = "10.18653/v1/P17-2020",
pages = "125--131",
abstract = "Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct HMM with neural network-based lexicon and alignment models, which are trained jointly using the Baum-Welch algorithm. The direct HMM is applied to rerank the n-best list created by a state-of-the-art phrase-based translation system and it provides improvements by up to 1.0{\%} Bleu scores on two different translation tasks.",
}
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%0 Conference Proceedings
%T Hybrid Neural Network Alignment and Lexicon Model in Direct HMM for Statistical Machine Translation
%A Wang, Weiyue
%A Alkhouli, Tamer
%A Zhu, Derui
%A Ney, Hermann
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F wang-etal-2017-hybrid
%X Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct HMM with neural network-based lexicon and alignment models, which are trained jointly using the Baum-Welch algorithm. The direct HMM is applied to rerank the n-best list created by a state-of-the-art phrase-based translation system and it provides improvements by up to 1.0% Bleu scores on two different translation tasks.
%R 10.18653/v1/P17-2020
%U https://aclanthology.org/P17-2020
%U https://doi.org/10.18653/v1/P17-2020
%P 125-131
Markdown (Informal)
[Hybrid Neural Network Alignment and Lexicon Model in Direct HMM for Statistical Machine Translation](https://aclanthology.org/P17-2020) (Wang et al., ACL 2017)
ACL