Phrase Level Segmentation and Labelling of Machine Translation Errors
F. Blain, V. Logacheva, and L. Specia. (2016) ``Phrase Level Segmentation and Labelling of Machine Translation Errors''. In Proceedings of LREC-2016
ESR6
MARMOT: A Toolkit for Translation Quality Estimation at the Word Level
V. Logacheva, C. Hokamp, and L. Specia. (2016) ``MARMOT: A Toolkit for Translation Quality Estimation at the Word Level''. In Proceedings of LREC-2016.
USFD's Phrase-level Quality Estimation Systems
V. Logacheva, F. Blain, and L. Specia. (2016) ``USFD's Phrase-level Quality Estimation Systems''. In Proceedings of the First Conference on Machine Translation (WMT-2016), Berlin, Germany, August 2016, pp. 800 - 805.
Findings of the 2016 Conference on Machine Translation
O. Bojar, R. Chatterjee, C. Federmann, Y. Graham, B. Haddow, M. Huck, A.J. Yepes, P. Koehn, V. Logacheva, C. Monz, M. Negri, A. Neveol, M. Neves, M. Popel, M. Post, R. Rubino, C. Scarton, L. Specia, M. Turchi, K. Verspoor and M. Zampieri. (2016) ``Findings of the 2016 Conference on Machine Translation''. In Proceedings of the First Conference on Machine Translation (WMT-2016), Berlin, Germany, August 2016, pp. 131--198.
Metrics for Evaluation of Word-level Machine Translation Quality Estimation
V. Logacheva, M. Lukasik and L. Specia. (2016) ``Metrics for Evaluation of Word-level Machine Translation Quality Estimation''. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-2016), Berlin, Germany, August 2016.
Phrase-level Quality Estimation for Machine Translation
V. Logacheva and L. Specia. (2015) “Phrase-level Quality Estimation for Machine Translation”. In Proceedings of the 12th International Workshop on Spoken Language Translation (IWSLT 2015), Da Nang, Vietnam, December 2015.
Marmot Word-level quality estimation
Submitted by c.orasan on Wed, 25/11/2015 - 11:46Marmot is a modular toolkit for extracting features and learning models. The toolkit is targeted at word-level quality estimation of machine translation output, but it can be used for any NLP task where features are extracted from raw text, and machine learning is done with the extracted features.
https://github.com/qe-team/marmot
Quality-based active sample selection strategy for statistical machine translation
Logacheva, V. and Specia, L. (2014). Quality-based active sample selection strategy for statistical machine translation. In Proceedings of the 9th edition of the Language Resources and Evaluation Conference (LREC2014), Reykjavik, Iceland, 26-31 May 2014
Confidence-based Active Learning Methods for Machine Translation
Logacheva, V. and Specia, L. (2014). Confidence-based Active Learning Methods for Machine Translation. In EACL-2014: the 14th Conference of the European Chapter of the Association for Computational Linguistics, Workshop on Humans and Computer-assisted Translation, Gothenburg, Sweden, 26 April, 2014.
Findings of the 2015 Workshop on Statistical Machine Translation
O. Bojar, R. Chatterjee, C. Federmann, B. Haddow, C. Hokamp, M. Huck, V. Logacheva, P. Koehn, C. Monz, M. Negri, P. Pecina, M. Post, C. Scarton, L. Specia, and M. Turchi (2015) “Findings of the 2015 Workshop on Statistical Machine Translation,” In Proceedings of the 10th Workshop on Machine Translation (WMT-2015), Lisbon, Portugal, pp. 1–46