Improving Translation Selection with a New Translation Model Trained by Independent Monolingual Corpora

We propose a novel statistical translation model to improve translation selection of collocation. In the statistical approach that has been popularly applied for translation selection, bilingual corpora are used to train the translation model. However, there exists a formidable bottleneck in acquiring large-scale bilingual corpora, in particular for language pairs involving Chinese. In this paper, we propose a new approach to training the translation model by using unrelated monolingual corpora. First, a Chinese corpus and an English corpus are parsed with dependency parsers, respectively, and two dependency triple databases are generated. Then, the similarity between a Chinese word and an English word can be estimated using the two monolingual dependency triple databases with the help of a simple Chinese-English dictionary. This cross-language word similarity is used to simulate the word translation probability. Finally, the generated translation model is used together with the language model trained with the English dependency database to realize translation of Chinese collocations into English. To demonstrate the effectiveness of this method, we performed various experiments with verb-object collocation translation. The experiments produced very promising results.