Class-based n-gram models of natural language
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Cube pruning as heuristic search
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Consensus training for consensus decoding in machine translation
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HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Simple, accurate parsing with an all-fragments grammar
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Exact decoding of syntactic translation models through Lagrangian relaxation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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ACM Transactions on Asian Language Information Processing (TALIP)
Post-Ordering by Parsing with ITG for Japanese-English Statistical Machine Translation
ACM Transactions on Asian Language Information Processing (TALIP)
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The intersection of tree transducer-based translation models with n-gram language models results in huge dynamic programs for machine translation decoding. We propose a multipass, coarse-to-fine approach in which the language model complexity is incrementally introduced. In contrast to previous order-based bigram-to-trigram approaches, we focus on encoding-based methods, which use a clustered encoding of the target language. Across various encoding schemes, and for multiple language pairs, we show speed-ups of up to 50 times over single-pass decoding while improving BLEU score. Moreover, our entire decoding cascade for trigram language models is faster than the corresponding bigram pass alone of a bigram-to-trigram decoder.