International Workshop on Spoken Language Translation (IWSLT) 2012

Hong Kong
December 6-7, 2012

Towards Contextual Adaptation for Any-text Translation

Li Gong, Aurélien Max, François Yvon

LIMSI-CNRS & Univ. Paris Sud, Orsay, France

Adaptation for Machine Translation has been studied in a variety of ways, using an ideal scenario where the training data can be split into ”out-of-domain” and ”in-domain” corpora, on which the adaptation is based. In this paper, we consider a more realistic setting which does not assume the availability of any kind of ”in-domain” data, hence the name ”any-text translation”. In this context, we present a new approach to contextually adapt a translation model onthe- fly, and present several experimental results where this approach outperforms conventionaly trained baselines. We also present a document-level contrastive evaluation whose results can be easily interpreted, even by non-specialists.

Full Paper   

Bibliographic reference.  Gong, Li / Max, Aurélien / Yvon, François (2012): "Towards contextual adaptation for any-text translation", In IWSLT-2012, 292-299.