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ACL
2015

Non-linear Learning for Statistical Machine Translation

3 years 10 months ago
Non-linear Learning for Statistical Machine Translation
Modern statistical machine translation (SMT) systems usually use a linear combination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit model on the current data. In this paper, we propose a nonlinear modeling for the quality of translation hypotheses based on neural networks, which allows more complex interaction between features. A learning framework is presented for training the non-linear models. We also discuss possible heuristics in designing the network structure which may improve the non-linear learning performance. Experimental results show that with the basic features of a hierarchical phrase-based machine translation system, our method produce translations that are better than a linear model.
Shujian Huang, Huadong Chen, Xinyu Dai, Jiajun Che
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Shujian Huang, Huadong Chen, Xinyu Dai, Jiajun Chen
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