Knowledge Commons of Institute of Automation,CAS
Towards Machine Translation in Semantic Vector Space | |
Jiajun Zhang; Shujie Liu; Mu Li; Ming Zhou; Chengqing Zong | |
发表期刊 | ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) |
2015-03 | |
卷号 | 14期号:2页码:26 |
摘要 |
Measuring the quality of the translation rules and their composition is an essential issue in the conventional statistical machine translation (SMT) framework. To express the translation quality, the previous lexical and phrasal probabilities are calculated only according to the co-occurrence statistics in the bilingual corpus and may be not reliable due to the data sparseness problem. To address this issue, we propose measuring the quality of the translation rules and their composition in the semantic vector embedding space (VES). We present a recursive neural network (RNN)-based translation framework, which includes two
submodels. One is the bilingually-constrained recursive auto-encoder, which is proposed to convert the lexical translation rules into compact real-valued vectors in the semantic VES. The other is a type-dependent recursive neural network, which is proposed to perform the decoding process by minimizing the semantic gap (meaning distance) between the source language string and its translation candidates at each state in a bottom-up structure. The RNN-based translation model is trained using a max-margin objective function that maximizes the margin between the reference translation and the n-best translations in forced decoding. In the experiments, we first show that the proposed vector representations for the translation rules are very reliable for application in translation modeling. We further show that the proposed type-dependent, RNN-based model can significantly improve the translation quality in the large-scale, end-to-end Chineseto-English translation evaluation. |
关键词 | Statistical Machine Translation Recursive Neural Network Semantic Meaning Distance Vector Embedding Space Max-margin Training |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40857 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
推荐引用方式 GB/T 7714 | Jiajun Zhang,Shujie Liu,Mu Li,et al. Towards Machine Translation in Semantic Vector Space[J]. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP),2015,14(2):26. |
APA | Jiajun Zhang,Shujie Liu,Mu Li,Ming Zhou,&Chengqing Zong.(2015).Towards Machine Translation in Semantic Vector Space.ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP),14(2),26. |
MLA | Jiajun Zhang,et al."Towards Machine Translation in Semantic Vector Space".ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 14.2(2015):26. |
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