This paper proposes a two-phase example-based machine translation methodology which develops translation templates from examples and then translates using template matching. This method improves translation quality and facilitates customization of machine translation systems. This paper focuses on the automatic learning of translation templates. A translation template is a bilingual pair of sentences in which corresponding units (words and phrases) are coupled and replaced with variables. Correspondence between units is determined by using a bilingual dictionary and by analyzing the syntactic structure of the sentences. Syntactic ambiguity and ambiguity in correspondence between units are simultaneously resolved. All of the translation templates generated from a bilingual corpus are grouped by their source language part, and then further refined to resolve conflicts among templates whose source language parts are the same but whose target language parts are different. By using the proposed method, not only transfer rules but also knowledge for lexical selection is effectively extracted from a bilingual corpus. One of the key issues in automatic learning is how to couple corresponding units (words and phrases) between bilingual texts. As far as we know, research done at BSO is the only work which has tackled this problem. [Sadlet90] To what degree this procedure can be automated, however, has not been made clear. We have independently developed an algorithm for coupling corresponding units in bilingual texts. This paper does not deal with the sentence aligning problem for bilingual texts, [Brown91] [Gale91] although this is important for automatic learning from translation examples. Rather, it discusses an algorithm for learning translation templates which assumes that a technique for parallel sentence alignment is available. Section 2 will present a rough sketch of our twophase example-based machine translation system. Sections 3, 4, and 5 will then describe the details of the algorithm for learning translation templates from translation examples. And finally Section 6 will discuss the features of the proposed system. Figure 1 outlines our two-phase example-based machine
In order to create translation templates from aligned translation pairs in REF , they parse the translation examples and align the syntactic units in the examples.
Learning Translation Templates From Bilingual Text
International Conference On Computational Linguistics
Computer Science