A systematic comparison of various statistical alignment models
Computational Linguistics
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
Supertagging: an approach to almost parsing
Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Tree-to-string alignment template for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
CCG supertags in factored statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Statistical Machine Translation
Statistical Machine Translation
Learning to translate with source and target syntax
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SemEval-2010 task 12: Parser evaluation using textual entailments
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Findings of the 2011 Workshop on Statistical Machine Translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Meteor 1.3: automatic metric for reliable optimization and evaluation of machine translation systems
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Feature-rich part-of-speech tagging for morphologically complex languages: application to Bulgarian
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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In this paper, we present our linguistically-enriched Bulgarian-to-English statistical machine translation model, which takes a statistical machine translation (SMT) system as backbone various linguistic features as factors. The motivation is to take advantages of both the robustness of the SMT system and the rich linguistic knowledge from morphological analysis as well as the hand-crafted grammar resources. The automatic evaluation has shown promising results and our extensive manual analysis confirms the high quality of the translation the system delivers. The whole framework is also extensible for incorporating information provided by different sources.