Machine translation divergences: a formal description and proposed solution
Computational Linguistics
Learning non-isomorphic tree mappings for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Machine translation using probabilistic synchronous dependency insertion grammars
ACL '05 Proceedings of the 43rd Annual Meeting on 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
Machine translation using probabilistic synchronous dependency insertion grammars
Machine translation using probabilistic synchronous dependency insertion grammars
Forest-based translation rule extraction
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Empirical lower bounds on alignment error rates in syntax-based machine translation
SSST '09 Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation
Phrase dependency parsing for opinion mining
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Learning to translate with source and target syntax
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Dependency forest for statistical machine translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Machine translation with lattices and forests
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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Translation requires non-isomorphic transformation from the source to the target. However, non-isomorphism can be reduced by learning multi-word units (MWUs). We present a novel way of representating sentence structure based on MWUs, which are not necessarily continuous word sequences. Our proposed method builds a simpler structure of MWUs than words using words as vertices of a dependency structure. Unlike previous studies, we collect many alternative structures in a packed forest. As an application of our proposed method, we extract translation rules in form of a source MWU-forest to the target string, and verify the rule coverage empirically. As a consequence, we improve the rule coverage compare to a previous work, while retaining the linear asymptotic complexity.