A Structural-Based Approach to Cantonese-English Machine Translation

In this paper, we present an integrated method to machine translation from Cantonese to English text. Our method combines example-based and rule-based methods that rely solely on example translations kept in a small Example Base (EB). One of the bottlenecks in example-based Machine Translation (MT) is a lack of knowledge or redundant knowledge in its bilingual knowledge base. In our method, a flexible comparison algorithm, based mainly on the content words in the source sentence, is applied to overcome this problem. It selects sample sentences from a small Example Base. The Example Base only keeps Cantonese sentences with different phrase structures. For the same phrase structure sentences, the EB only keeps the most simple sentence. Target English sentences are constructed with rules and bilingual dictionaries. In addition, we provide a segmentation algorithm for MT. A feature of segmentation algorithm is that it not only considers the source language itself but also its corresponding target language. Experimental results show that this segmentation algorithm can effectively decrease the complexity of the translation process.