Neural Network-based Reranking Model for Statistical Machine Translation
The non-local feature always plays an important role in improving performance of SMT.Nonlinear neural network model can take better advantage of non-local features to improve the performance of translation through the introduction of the hidden layer.So this paper will build reranking models based on neural network to make use of non-local features to improve the translation performance.In this paper,we will introduce two models: Reranker-WC and Reranker-D.Compared with performance of the baseline system,the performance of Reranker-WC can be promoted to about 1.4 BLEU score.Moreover,we find that different hyper-parameter λ will also affect the quality of SMT output at the same time.We achieve the best performance while λ is 40.
SMT reranking neural network conjugate gradient method
Haipeng Sun Tiejun Zhao
School of Computer Science and Technology Harbin Institute of Technology(HIT),Harbin,China
国际会议
厦门
英文
469-474
2014-08-19(万方平台首次上网日期,不代表论文的发表时间)