Using Context Vectors in Improving a Machine Translation System with Bridge Language

Samira Tofighi Zahabi, Somayeh Bakhshaei, Shahram Khadivi

Abstract: Mapping phrases between languages as translation of each other by using an intermediate language (pivot language) may generate translation pairs that are wrong. Since a word or a phrase has different meanings in different contexts, we should map source and target phrases in an intelligent way. We propose a pruning method based on the context vectors to remove those phrase pairs that connect to each other by a polysemous pivot phrase or by weak translations. We use context vectors to implicitly disambiguate the phrase senses and to recognize irrelevant phrase translation pairs. Using the proposed method a relative improvement of 2.8 percent in terms of BLEU score is achieved.