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.
Cite as: Gong, L., Max, A., Yvon, F. (2012) Towards contextual adaptation for any-text translation. Proc. International Workshop on Spoken Language Translation (IWSLT 2012), 292-299
@inproceedings{gong12_iwslt,
author={Li Gong and Aurélien Max and François Yvon},
title={{Towards contextual adaptation for any-text translation}},
year=2012,
booktitle={Proc. International Workshop on Spoken Language Translation (IWSLT 2012)},
pages={292--299}
}