Online multi-user adaptive statistical machine translation
Prashant Mathur, Mauro Cettolo, Marcello Federico, José G.C. de Souza
Abstract
In this paper we investigate the problem of adapting a machine translation system to the feedback provided by multiple post-editors. It is well know that translators might have very different post-editing styles and that this variability hinders the application of online learning methods, which indeed assume a homogeneous source of adaptation data. We hence propose multi-task learning to leverage bias information from each single post-editors in order to constrain the evolution of the SMT system. A new framework for significance testing with sentence level metrics is described which shows that Multi-Task learning approaches outperforms existing online learning approaches, with significant gains of 1.24 and 1.88 TER score over a strong online adaptive baseline, on a test set of post-edits produced by four translators texts and on a popular benchmark with multiple references, respectively.- Anthology ID:
- 2014.amta-researchers.12
- Volume:
- Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
- Month:
- October 22-26
- Year:
- 2014
- Address:
- Vancouver, Canada
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 152–165
- Language:
- URL:
- https://aclanthology.org/2014.amta-researchers.12
- DOI:
- Cite (ACL):
- Prashant Mathur, Mauro Cettolo, Marcello Federico, and José G.C. de Souza. 2014. Online multi-user adaptive statistical machine translation. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 152–165, Vancouver, Canada. Association for Machine Translation in the Americas.
- Cite (Informal):
- Online multi-user adaptive statistical machine translation (Mathur et al., AMTA 2014)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2014.amta-researchers.12.pdf