Neural Network-based Reranking Model for Statistical Machine Translation

摘要：

　　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.

关键词： SMT reranking neural network conjugate gradient method

作者: Haipeng Sun Tiejun Zhao

作者单位: School of Computer Science and Technology Harbin Institute of Technology(HIT),Harbin,China

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 469-474

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）