Translating Collocation using Monolingual and Parallel Corpus

In this paper, we propose a method for translating a given verb-noun collocation based on a parallel corpus and an additional monolingual corpus. Our approach involves two models to generate collocation translations. The combination translation model generates combined translations of the collocate and the base word, and filters translations by a target language model from a monolingual corpus, and the bidirectional alignment translation model generates translations using bidirectional alignment information. At run time, each model generates a list of possible translation candidates, and translations in two candidate lists are re-ranked and returned as our system output. We describe the implementation of using method using Hong Kong Parallel Text. The experiment results show that our method improves the quality of top-ranked collocation translations, which could be used to assist ESL learners and bilingual dictionaries editors. Keyword: collocation, statistical machine translation, computer-assisted translation

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