Learning Reordering Models for Statistical Machine Translation with a Pivot Language
نویسندگان
چکیده
This paper presents a work related to reordering when dealing with translation using pivot languages. Different pivot strategies are presented in order to compare their translation quality on a ChineseSpanish task. A novel method to generate reordering weights automatically for a language pair that do not share parallel corpus is presented. Experiments which show that the strategy outperforms the cascade approach for pivot translation are reviewed.
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