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)
38 Chapter 1. 机器翻译简介 肖桐 朱靖波
转换”这个过程,这一点与基于转换的方法有很大区别。
语言 1
语言 3
语言 2
语言 4
中间语言
(a) 基于中间语言的方法
语言 1
语言 3
语言 2
语言 4
(b) 基于转换的方法
图 1.12 基于中间语言的方法 (a) 与基于转换的方法 (b)
从图1.9可以发现,中间语言(知识表示)处于最顶端,本质上是独立于源语言
和目标语言的,这也是基于中间语言的方法可以将分析过程和生成过程分开的原因。
虽然基于中间语言的方法有上述优点,但如何定义中间语言是一个关键问题。严
格上说,所谓中间语言本身是一种知识表示结构,承载着源语言句子的分析结果,应
该包含和体现尽可能多的源语言知识。如果中间语言的表示能力不强,会导致源语
言句子信息丢失,这自然会影响目标语言生成结果。
在基于规则的机器翻译方法中,构建中间语言结构的知识表示方式有很多,比
较常见的是语法树、语义网、逻辑结构表示或者多种结构的融合等。但不管哪种方
法,实际上都无法充分地表达源语言句子所携带的信息。因此,在早期的基于规则
的机器翻译研究中,基于中间语言的方法明显弱于基于转换的机器翻译方法。不过,
近些年随着神经机器翻译等方法的兴起,使用统一的中间表示来刻画句子又受到了
广泛关注。但是,神经机器翻译中的“中间表示”并不是规则系统中的中间语言,二
者有着本质区别,这部分内容将会在第十章进行介绍。
1.4.4 规则方法的优缺点
在基于规则的机器翻译时代,机器翻译技术研究有一个特点就是语法(Grammer)
和算法(Algorithm)分开,相当于是把语言分析和程序设计分开。传统方式使用程
序代码来实现翻译规则,并把所谓的翻译规则隐含在程序代码实现中。其中最大问
题是一旦翻译规则发生修改,程序代码也需要进行相应修改,导致维护代价非常高。
此外书写翻译规则的语言学家与编代码的程序员沟通代价也非常高,有时候会出现
鸡同鸭讲的感觉。把语法和算法分开对于基于规则的机器翻译技术来说最大好处就
是可以将语言学家和程序员的工作分开,各自发挥自己的优势。
这种语言分析和程序设计分开的实现方式也使得基于人工书写翻译规则的机器
翻译方法非常直观,语言学家可以很容易地将翻译知识利用规则的方法表达出来,并
且不需要修改系统代码。例如:1991 年,东北大学自然语言处理实验室王宝库教授
提出的规则描述语言(CTRDL)
[18]
。以及 1995 年,同为东北大学自然语言处理实验