The Maximum Entropy based Rule Selection Model for Statistical Machine Translation (Invited Paper)

2008 ◽  
Author(s):  
Qun Liu ◽  
Zhongjun He
Keyword(s):  
Maximum Entropy ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Selection Model ◽  
Rule Selection

scholarly journals A Continuous Space Rule Selection Model for Syntax-based Statistical Machine Translation

2016 ◽  
Author(s):  
Jingyi Zhang ◽  
Masao Utiyama ◽  
Eiichro Sumita ◽  
Graham Neubig ◽  
Satoshi Nakamura
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Selection Model ◽  
Continuous Space ◽  
Rule Selection

scholarly journals Topic-Based Dissimilarity and Sensitivity Models for Translation Rule Selection

2014 ◽  
Vol 50 ◽  
pp. 1-30 ◽  
Author(s):  
M. Zhang ◽  
X. Xiao ◽  
D. Xiong ◽  
Q. Liu
Keyword(s):  
Machine Translation ◽  
Topic Model ◽  
Statistical Machine Translation ◽  
Model Space ◽  
Target Language ◽  
Translation Quality ◽  
Rule Selection ◽  
Translation Rule ◽  
Selection Experiments ◽  
Target Side

Translation rule selection is a task of selecting appropriate translation rules for an ambiguous source-language segment. As translation ambiguities are pervasive in statistical machine translation, we introduce two topic-based models for translation rule selection which incorporates global topic information into translation disambiguation. We associate each synchronous translation rule with source- and target-side topic distributions.With these topic distributions, we propose a topic dissimilarity model to select desirable (less dissimilar) rules by imposing penalties for rules with a large value of dissimilarity of their topic distributions to those of given documents. In order to encourage the use of non-topic specific translation rules, we also present a topic sensitivity model to balance translation rule selection between generic rules and topic-specific rules. Furthermore, we project target-side topic distributions onto the source-side topic model space so that we can benefit from topic information of both the source and target language. We integrate the proposed topic dissimilarity and sensitivity model into hierarchical phrase-based machine translation for synchronous translation rule selection. Experiments show that our topic-based translation rule selection model can substantially improve translation quality.

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