Decoding complexity in word-replacement translation models
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
ORANGE: a method for evaluating automatic evaluation metrics for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The complexity of phrase alignment problems
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
iROVER: improving system combination with classification
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Comparing reordering constraints for SMT using efficient Bleu oracle computation
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
Word graphs for statistical machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Online large-margin training of syntactic and structural translation features
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Decomposability of translation metrics for improved evaluation and efficient algorithms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Assessing phrase-based translation models with oracle decoding
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Optimal search for minimum error rate training
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Computing lattice BLEU oracle scores for machine translation
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Oracle decoding as a new way to analyze phrase-based machine translation
Machine Translation
Lattice BLEU oracles in machine translation
ACM Transactions on Speech and Language Processing (TSLP)
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Confusion networks are a simple representation of multiple speech recognition or translation hypotheses in a machine translation system. A typical operation on a confusion network is to find the path which minimizes or maximizes a certain evaluation metric. In this article, we show that this problem is generally NP-hard for the popular BLEU metric, as well as for smaller variants of BLEU. This also holds for more complex representations like generic word graphs. In addition, we give an efficient polynomial-time algorithm to calculate unigram BLEU on confusion networks, but show that even small generalizations of this data structure render the problem to be NP-hard again. Since finding the optimal solution is thus not always feasible, we introduce an approximating algorithm based on a multi-stack decoder, which finds a (not necessarily optimal) solution for n-gram BLEU in polynomial time.