An algorithm for pronominal anaphora resolution
Computational Linguistics
Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
A systematic comparison of various statistical alignment models
Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Paraphrasing with bilingual parallel corpora
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Paraphrasing for automatic evaluation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of 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
An unsupervised approach for bootstrapping Arabic sense tagging
Semitic '04 Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages
Rich source-side context for statistical machine translation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Projecting parameters for multilingual word sense disambiguation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Aiding pronoun translation with co-reference resolution
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Findings of the 2011 Workshop on Statistical Machine Translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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Machine Translation is a well--established field, yet the majority of current systems translate sentences in isolation, losing valuable contextual information from previously translated sentences in the discourse. One important type of contextual information concerns who or what a coreferring pronoun corefers to (i.e., its antecedent). Languages differ significantly in how they achieve coreference, and awareness of antecedents is important in choosing the correct pronoun. Disregarding a pronoun's antecedent in translation can lead to inappropriate coreferring forms in the target text, seriously degrading a reader's ability to understand it. This work assesses the extent to which source-language annotation of coreferring pronouns can improve English--Czech Statistical Machine Translation (SMT). As with previous attempts that use this method, the results show little improvement. This paper attempts to explain why and to provide insight into the factors affecting performance.