融合目标语言端语义角色的串到树翻译模型

苏超; 郭宇航; 黄河燕; 史树敏; 冯冲

doi:10.1631/FITEE.1601349

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融合目标语言端语义角色的串到树翻译模型

常规文章 | Updated：2022-05-19

- 融合目标语言端语义角色的串到树翻译模型
- Incorporating target language semantic roles into a string-to-tree translation model
- 信息与电子工程前沿（英文） 2017年18卷第10期页码：1534-1542
- Affiliations：
  
  School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
  Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing 100081, China
  Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
- Author bio：
  
  [ "SU Chao, E-mail: suchao@bit.edu.cn" ]
  He-yan HUANG, E-mail: hhy63@bit.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61132009, 61201352, 61502035, and 61201351), the National Basic Research Program (973) of China (No. 2013CB329303), and the Beijing Advanced Innovation Center for Imaging Technology (No. BAICIT-2016007)
- DOI：10.1631/FITEE.1601349
  中图分类号： TP391
- 收稿：2016-06-18，
  
  修回：2017-11-;3，
  
  纸质出版：2017-10
- Accepted：
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苏超, 郭宇航, 黄河燕, 等. 融合目标语言端语义角色的串到树翻译模型[J]. 信息与电子工程前沿（英文）, 2017,18(10):1534-1542.

Chao SU, Yu-hang GUO, He-yan HUANG, et al. Incorporating target language semantic roles into a string-to-tree translation model[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1534-1542.
苏超, 郭宇航, 黄河燕, 等. 融合目标语言端语义角色的串到树翻译模型[J]. 信息与电子工程前沿（英文）, 2017,18(10):1534-1542. DOI： 10.1631/FITEE.1601349.

Chao SU, Yu-hang GUO, He-yan HUANG, et al. Incorporating target language semantic roles into a string-to-tree translation model[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1534-1542. DOI： 10.1631/FITEE.1601349.

摘要

串到树模型是统计机器翻译中最为成功的基于句法的模型之一。它通过对目标语言端句法信息进行建模，使得机器输出的译文更符合语法。然而，它并未利用任何语义信息，产生的译文仍然包含语义角色混淆和语块顺序混乱等错误。提出两种方式，利用语义角色提高串到树模型性能：（1）在句法树上添加语义角色标签；（2）先将语义角色转换成树结构，再引入句法信息。将上述两种新的树结构用于串到树机器翻译模型训练，使得系统能够利用语义信息学习或选择更好的翻译规则。实验表明，在口语和新闻两种语料上，我们的方法都超越了传统串到树翻译系统；在大规模新闻语料上，我们的方法超越了基于短语的机器翻译系统。

Abstract

The string-to-tree model is one of the most successful syntax-based statistical machine translation (SMT) models. It models the grammaticality of the output via target-side syntax. However

it does not use any semantic information and tends to produce translations containing semantic role confusions and error chunk sequences. In this paper

we propose two methods to use semantic roles to improve the performance of the string-to-tree translation model: (1) adding role labels in the syntax tree; (2) constructing a semantic role tree

and then incorporating the syntax information into it. We then perform string-to-tree machine translation using the newly generated trees. Our methods enable the system to train and choose better translation rules using semantic information. Our experiments showed significant improvements over the state-of-the-art string-to-tree translation system on both spoken and news corpora

and the two proposed methods surpass the phrase-based system on large-scale training data.

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