With this approach they improve the translation quality over the usage of large generic translation models.
we assume that we have a multilingual parallel corpus consisting of sentences, x l i in a language l, grouped into parallel translations: we also assume that we have a collection of wordnets consisting of a set of senses, w l ij , grouped into synsets, for each language: we say that a context x l 0 i , in language l 0 (in our case this is always English), is disambiguated in n languages for a word w l 0 jk if: a context is disambiguated in n languages for a word, if for each of its translations we have a context in the parallel corpus that contains one of the known synset translations.