In this paper, we give a description of the machine translation system developed at DCU that was used for our second participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2007). In this participation, we focus on some new methods to improve system quality. Specifically, we try our word packing technique for different language pairs, we smooth our translation tables with out-of-domain word translations for the Arabic\u2013English and Chinese\u2013English tasks in order to solve the high number of out of vocabulary items, and finally we deploy a translation-based model for case and punctuation restoration.<\/p>\n
We participated in both the classical and challenge tasks for the following translation directions: Chinese\u2013English, Japanese\u2013English and Arabic\u2013English. For the last two tasks, we translated both the single-best ASR hypotheses and the correct recognition results; for Chinese\u2013 English, we just translated the correct recognition results. We report the results of the system for the provided evaluation sets, together with some additional experiments carried out following identification of some simple tokenisation errors in the official runs.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we give a description of the machine translation system developed at DCU that was used for our second participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2007). In this participation, we focus on some new methods to improve system quality. 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