Tree Kernels for Machine Translation Quality Estimation

WMT 2012 - 7th Workshop on Statistical Machine Translation, Proceedings of the Workshop

Volumn , Issue , 2012, Pages 109-113

  Hardmeier, Christian ^a   Nivre, Joakim ^a   Tiedemann, J¨org ^a  

^a UPPSALA UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; FORESTRY; MACHINE TRANSLATION;

DEPENDENCY PARSER; ESTIMATION SYSTEMS; INPUT MODELS; INPUT-OUTPUT; MACHINE TRANSLATIONS; QUALITY ESTIMATION; SUPPORT VECTOR MACHINE REGRESSIONS; TRANSLATION QUALITY; TREE KERNELS; UPPSALA UNIVERSITY;

COMPUTER AIDED LANGUAGE TRANSLATION;

EID: 85018125682     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Chris Callison-Burch, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut, and Lucia Specia. 2012. Findings of the 2012Workshop on Statistical Machine Translation. In Proceedings of the Seventh Workshop on Statistical Machine Translation, Montreal, Canada, June. Association for Computational Linguistics.
2. Michael Collins and Nigel Duffy. 2001. Convolution kernels for natural language. In Proceedings of NIPS 2001, pages 625-632.
3. Jeśus Giḿenez and Llúis Marquez. 2004. SVMTool: A general POS tagger generator based on Support Vector Machines. In Proceedings of the 4th Conference on International Language Resources and Evaluation (LREC-2004), Lisbon.
4. Ṕeter Haĺacsy, Andŕas Kornai, and Csaba Oravecz. 2007. HunPos - an open source trigram tagger. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics. Companion Volume: Proceedings of the Demo and Poster Sessions, pages 209- 212, Prague, Czech Republic, June. Association for Computational Linguistics.
5. Christian Hardmeier. 2011. Improving machine translation quality prediction with syntactic tree kernels. In Mikel L. Forcada, Heidi Depraetere, and Vincent Vandeghinste, editors, Proceedings of the 15th conference of the European Association for Machine Translation (EAMT 2011), pages 233-240, Leuven, Belgium.
6. Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. 2010. A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University.
7. Thorsten Joachims. 1999. Making large-scale SVM learning practical. In B. Sch¨olkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning. MIT Press.
8. Richard Johansson and Alessandro Moschitti. 2010. Syntactic and semantic structure for opinion expression detection. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pages 67-76, Uppsala, Sweden, July. Association for Computational Linguistics.
9. Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pages 423-430, Sapporo, Japan, July. Association for Computational Linguistics.
10. Alessandro Moschitti. 2006a. Efficient convolution kernels for dependency and constituent syntactic trees. In Proceedings of the 17th European Conference on Machine Learning, Berlin.
11. Alessandro Moschitti. 2006b. Making tree kernels practical for natural language learning. In Proceedings of the Eleventh International Conference of the European Association for Computational Linguistics, Trento.
12. Joakim Nivre, Johan Hall, and Jens Nilsson. 2006. MaltParser: A language-independent system for datadriven dependency parsing. In Proceedings of the 5th Conference on International Language Resources and Evaluation (LREC-2006), pages 2216-2219, Genoa.
13. Lucia Specia, Craig Saunders, Marco Turchi, Zhuoran Wang, and John Shawe-Taylor. 2009. Improving the confidence of Machine Translation quality estimates. In Proceedings of MT Summit XII, Ottawa.
14. J¨org Tiedemann. 2009. News from OPUS - a collection of multilingual parallel corpora with tools and interface. In N. Nicolov, K. Bontcheva, G. Angelova, and R. Mitkov, editors, Recent Advances in Natural Language Processing, pages 237-248. John Benjamins, Amsterdam.