【作者】 蒋宏飞；

【导师】 杨沐昀；

【作者基本信息】 哈尔滨工业大学 ， 计算机科学与技术， 2006， 硕士

【摘要】 基于实例的机器翻译方法具有系统实现周期短,容易对新的知识进行扩充,在限定领域下可以生成高质量的译文的优点。但是,由于在EBMT系统进行译文搜索的过程中往往只能依靠人为设定的启发式函数进行指导,对人为因素的依赖较大,很容易造成对某个限定领域特点的过度拟合。在系统进行领域移植时性能难以保证。而且,通常的EBMT翻译模型中难以对丰富的特征信息进行建模,使得EBMT的翻译性能受到很大限制。本文利用机器学习算法(最大熵方法)对EBMT的翻译模型进行了自动构建尝试。与以往依赖人为的启发式搜索函数不同,本文采用的方法在最大熵方法的构架下,融入了丰富的特征信息,力图构造出一个多维的特征空间,对翻译模型的一般特征进行整体建模。在模型训练过程中,为了尽量得到比较完整的译文空间,本文设计了一个可以控制的译文搜索算法。在对译文进行自动评价的过程中,本文根据需要对单句进行评价的需求,对仅适合于对整篇文档进行自动评价的评测指标BLEU进行了三个方面的修正。这种修正对相关的研究也很有借鉴意义。实验结果初步表明,本文提出的基于最大熵构架的EBMT翻译模型在性能上超过了原来的实验平台系统,同时也证明了丰富的特征有助于EBMT翻译性能的提高。在各种类型特征对性能的贡献分析中发现,在开放测试中词一级的特征对系统性能的贡献最大。更多还原

【Abstract】 The Example-Based Machine Translation (EBMT) system can be developed in a short period with relatively better translation result for a given domain. In translation searching process, many EBMT systems can only rely on a heuristic guidance which is given by human. Therefore that process will not be objective and always lean much on the intuition of system developer and may be overfitting to a special domain. It is hard to maintain the performance when the system is transplanted to other domains. Moreover, the general EBMT system can not model the rich features in translation process.This thesis tries to construct the translation model of EBMT automatically by using machine learning algorithm (here, Maximum Entropy). In order to adequately incorporate different kinds of information which can be explored from examples, this thesis introduces a log-linear translation model into EBMT. In addition, a high dimensional feature space is formally constructed to include general features of different aspects.In order to get the translations space as full as possible, a controllable search algorithm has been proposed in this thesis. The translation auto-evaluation metric BLEU is suitable for whole document translation evaluation. But many problems will arise when the evaluation objective is single sentence. To meet the need of single sentence evaluation in the research, this thesis presents an amendment to BLEU metric, which is also useful for related research topics.The preliminary experimental results indicate that the maximum entropy framework based EBMT translation model proposed in this thesis outperforms the base system and the rich features can supply much useful information for translation process. In contribution analysis, the result shows that word-level features are the most useful information.更多还原