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