会议专题

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

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)

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)