Toward Memory-based Translation

An essential problem of example-based translation is how to utilize more than one translation example for translating one source sentence. This 1)aper proposes a method to solve this problem. We introduce tile representation, called .matching e,,z:pressio~z, which tel)resents the combination of fragments of translation examples. The translation process consists of three steps: (.1) Make the source matching expression from lhe source sentence. (2) TransDr the source matching expression into the target matching expression. (3) Construct the target sentence from the target matching expression. This mechanism generates some candidates of translation. To select, the best translation out of them, we define the score of a translation. 1 I n t r o d u c t i o n Use of extracted information fiom examples or example-based translation is becoming the new wave of machine translation. The ba.sic idea. of example~based translation is very simple: translate a source sentence by imitating the translation example of a similar sentence in the database. The idea first appeared in [Nagao 84], and some research has followed it [Sumita 88][Sato 89][Sadler 89a.][Sadler 89b]. But a great deal of effort is still needed to implemenl the idea. In our previous work, we show how to select. the best target word in case-frame translation based on examples[Sato 89]. In this paper, we concentrate on two problems: 1. ltow to combine some fragments of translation examph~s in order to translate one sentence? 2. tlow to select tile best tra.nslation out of inany candidates? We show partial solutions for them in MBT2. MBT2 is the second prototype system in our Memory-based Translation Project.. MBT2 ca.n do bi-directional m~nslation between an English word-dependency tree and a Japanese worddependency tree. It is implemented in Sicstus Prolog. 2 N e e d to C o m b i n e F r a g m e nt s The basic idea of example-based translation is very simple: translate a source sentence by imitating the translation example of similar sent ence in the database. But in many cases, it is necessary to imitate more than one translation example and combine some fragments of them. Let's consider the translation of the following sentence. (1) He buys a book on international politics. If we know the following translation examt)le (2) and (3), we can translate sentence (1) into sentence (4) by imitating examples and colnbining fragments of them. (2) He buys a notebook. Kate ha nouto wo ka~. (3) I read a boo]~ on international polilics. Watt, hi ha kokusaiseiji nit,suite l:akareta hon wo yomu. (4) Kate ha kokusMseiji nitsuite kM~reta hon WO ka~ll. It is easy for a human to do this, but not so for a machine. The ability to combine some fragments of translation examples is essential to example-based translation. A lack of this ability restricts the power of example-based translation. In this paper, we concentrate on the implementation of this ability on machine.