Abstract
We present a new version of QUEST ― an open source framework for machine translation quality estimation ― which brings a number of improvements: (i) it provides a Web interface and functionalities such that non-expert users, e.g. translators or lay-users of machine translations, can get quality predictions (or internal features of the framework) for translations without having to install the toolkit, obtain resources or build prediction models; (ii) it significantly improves over the previous runtime performance by keeping resources (such as language models) in memory; (iii) it provides an option for users to submit the source text only and automatically obtain translations from Bing Translator; (iv) it provides a ranking of multiple translations submitted by users for each source text according to their estimated quality. We exemplify the use of this new version through some experiments with the framework.- Anthology ID:
- L14-1726
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/964_Paper.pdf
- DOI:
- Cite (ACL):
- Kashif Shah, Marco Turchi, and Lucia Specia. 2014. An efficient and user-friendly tool for machine translation quality estimation. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- An efficient and user-friendly tool for machine translation quality estimation (Shah et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/964_Paper.pdf