Cross-lingual alignment transfer: a chicken-and-egg story?

International audienceIn this paper, we challenge a basic assumption of many cross-lingual transfer techniques: the availability of word aligned parallel corpora, and consider ways to accommodate situations in which such resources do not exist. We show experimentally that, here again, weakly supervised cross-lingual learning techniques can prove useful, once adapted to transfer knowledge across pairs of languages

Improving Word Alignment with Bridge Languages

We describe an approach to improve Statistical Machine Translation (SMT) performance using multi-lingual, parallel, sentence-aligned corpora in several bridge languages. Our approach consists of a simple method for utilizing a bridge language to create a word alignment system and a procedure for combining word alignment systems from multiple bridge languages. The final translation is obtained by consensus decoding that combines hypotheses obtained using all bridge language word alignments. We present experiments showing that multilingual, parallel text in Spanish, French, Russian, and Chinese can be utilized in this framework to improve translation performance on an Arabic-to-English task.