Abstract
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.- Anthology ID:
- 2012.iwslt-papers.20
- Volume:
- Proceedings of the 9th International Workshop on Spoken Language Translation: Papers
- Month:
- December 6-7
- Year:
- 2012
- Address:
- Hong Kong, Table of contents
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Note:
- Pages:
- 292–299
- Language:
- URL:
- https://aclanthology.org/2012.iwslt-papers.20
- DOI:
- Cite (ACL):
- Li Gong, Aurélien Max, and François Yvon. 2012. Towards contextual adaptation for any-text translation. In Proceedings of the 9th International Workshop on Spoken Language Translation: Papers, pages 292–299, Hong Kong, Table of contents.
- Cite (Informal):
- Towards contextual adaptation for any-text translation (Gong et al., IWSLT 2012)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2012.iwslt-papers.20.pdf