Machine Translation within One Language as a Paraphrasing Technique Online.
Petra Baranċíková, Aleš Tamchyna · ITAT · 2014
We present a method for improving machine translation (MT) evaluation by targeted paraphrasing of reference sentences. For this purpose, we employ MT sys- tems themselves and adapt them for translating within a single language. We describe this attempt on two types of MT systems - phrase-based and rule-based. Initially, we experiment with the freely available SMT system Moses. We create translation models from two available sources of Czech paraphrases - Czech WordNet and the Meteor Para- phrase tables. We extended Moses by a new feature that makes the translation targeted. However, the results of this method are inconclusive. In the view of errors appearing in the new paraphrased sentences, we propose another so- lution - targeted paraphrasing using parts of a rule-based translation system included in the NLP framework Treex.