A systematic comparison of various statistical alignment models
Computational Linguistics
Natural Language Engineering
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
An unsupervised method for word sense tagging using parallel corpora
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Exploiting parallel texts for word sense disambiguation: an empirical study
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
HLT '91 Proceedings of the workshop on Speech and Natural Language
Word sense disambiguation vs. statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
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
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Mixture-model adaptation for SMT
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Context-aware discriminative phrase selection for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Experiments in domain adaptation for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Rich source-side context for statistical machine translation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Bilingual sense similarity for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Example-based paraphrasing for improved phrase-based statistical machine translation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Cache-based document-level statistical machine translation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Encouraging consistent translation choices
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Extending machine translation evaluation metrics with lexical cohesion to document level
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
The trouble with SMT consistency
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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We revisit the one sense per discourse hypothesis of Gale et al. in the context of machine translation. Since a given sense can be lexicalized differently in translation, do we observe one translation per discourse? Analysis of manual translations reveals that the hypothesis still holds when using translations in parallel text as sense annotation, thus confirming that translational differences represent useful sense distinctions. Analysis of Statistical Machine Translation (SMT) output showed that despite ignoring document structure, the one translation per discourse hypothesis is strongly supported in part because of the low variability in SMT lexical choice. More interestingly, cases where the hypothesis does not hold can reveal lexical choice errors. A preliminary study showed that enforcing the one translation per discourse constraint in SMT can potentially improve translation quality, and that SMT systems might benefit from translating sentences within their entire document context.