Computational Complexity of Problems on Probabilistic Grammars and Transducers
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Decoding complexity in word-replacement translation 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
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Scalable inference and training of context-rich syntactic translation models
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A better N-best list: practical determinization of weighted finite tree automata
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Hierarchical Phrase-Based Translation
Computational Linguistics
A systematic comparison of phrase-based, hierarchical and syntax-augmented statistical MT
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Lattice Minimum Bayes-Risk decoding for statistical machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Syntax augmented machine translation via chart parsing
StatMT '06 Proceedings of the Workshop on 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
Jane: open source hierarchical translation, extended with reordering and lexicon models
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Improved translation with source syntax labels
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning hierarchical translation structure with linguistic annotations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Improving decoding generalization for tree-to-string translation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Automatic category label coarsening for syntax-based machine translation
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
Jane: an advanced freely available hierarchical machine translation toolkit
Machine Translation
DFKI's SMT system for WMT 2012
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
| Hi-index | 0.00 |
We propose a novel probabilistic synchoronous context-free grammar formalism for statistical machine translation, in which syntactic nonterminal labels are represented as "soft" preferences rather than as "hard" matching constraints. This formalism allows us to efficiently score unlabeled synchronous derivations without forgoing traditional syntactic constraints. Using this score as a feature in a log-linear model, we are able to approximate the selection of the most likely unlabeled derivation. This helps reduce fragmentation of probability across differently labeled derivations of the same translation. It also allows the importance of syntactic preferences to be learned alongside other features (e.g., the language model) and for particular labeling procedures. We show improvements in translation quality on small and medium sized Chinese-to-English translation tasks.