[1]陈铬亮,徐佳.基于单词分类的归一化神经网络语言模型研究[J].福建工程学院学报,2016,14(04):382-385.[doi:10.3969/j.issn.1672-4348.2016.04.014]
　Chen Geliang,Xu Jia.Research on word classification-based normalized neural network language model[J].Journal of FuJian University of Technology,2016,14(04):382-385.[doi:10.3969/j.issn.1672-4348.2016.04.014]
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基于单词分类的归一化神经网络语言模型研究()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第14卷
期数:
2016年04期
页码:
382-385
栏目:
出版日期:
2016-08-25

文章信息/Info

Title:
Research on word classification-based normalized neural network language model
作者:
陈铬亮; 徐佳
清华大学交叉信息研究院
Author(s):
Chen Geliang;  Xu Jia
IIIS, Tsinghua University
关键词:
机器翻译;  语言模型;  单词分类
Keywords:
machine translation;  language model;  word classification
分类号:
TP391.2
DOI:
10.3969/j.issn.1672-4348.2016.04.014
文献标志码:
A
摘要:
提出了一种基于单词分类的神经网络语言模型，以解决归一化问题。实验方法为，在基础翻译系统中加入模型参数，然后利用开发集调整参数，再对测试集进行翻译，对比加入模型参数前后的翻译质量以及训练模型和翻译过程所需时间。实验结果表明，在保证归一化的前提下，该模型的性能优于Vaswani等人的模型，且翻译质量与Vaswani等人的模型相当。
Abstract:
A word classification-based neural network language model was proposed to resolve normalization problems. Model parameters were introduced to the basic translation system, which were adjusted by development sets. The test sets were translated. The translation quality and training model and the time taken by the translation were compared. The results indicate that the model is superior to that of Vasvani in performance with its translation quality being similar to that of Vasvani.

参考文献/References:

[1] Shannon C E. Prediction and entropy of printed English[J]. Bell System Technical Journal, 1951, 30(1):50-64.
[2] Vaswani A, Zhao Y, Fossum V, et al. Decoding with large-scale neural language models improves translation[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Seattle, America: Association for Computational Linguistics,2013:1387-1392.
[3] Mnih A, Teh Y W. A fast and simple algorithm for training neural probabilistic language models[C]//Proceedings of the 29th International Conference on Machine Learning. Edinburgh: International Machine Learning Society,2012:1751-1758.
[4] Kneser R, Ney H. Improved clustering techniques for class-based statistical language modelling[C]//Eurospeech’93. Berlin, Germany: International Speech Communication Association,1993:973-976.
[5] Och F J, Ney H. A systematic comparison of various statistical alignment models//[J]. Computational Linguistics, 2003, 29(1):19-51.
[6] Koehn P, Hoang H, Birch A, et al. Moses: Open source toolkit for statistical machine translation[C]//Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions. Prague, Czech: Association for Computational Linguistics, 2007.177-180.
[7] Koehn P, Och F J, Marcu D. Statistical phrase-based translation[C]// Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. Edmonton, Canada: Association for Computational Linguistics, 2003.127-133.
[8] Och F J. Statistical machine translation : from single word models to alignment templates[J]. Rwth Aachen, 2002, 10(2):65-70.
[9] Papineni K, Roukos S, Ward T, et al. BLEU: a method for automatic evaluation of machine translation[C]// Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Philadelphia, America: Association for Computational Linguistics, 2002.311-318.

更新日期/Last Update: 2016-08-25