A statistical approach to machine translation
Computational Linguistics
A maximum entropy approach to natural language processing
Computational Linguistics
The syntactic process
Word reordering and a dynamic programming beam search algorithm for statistical machine translation
Computational Linguistics
Exploiting syntactic structure for natural language modeling
Exploiting syntactic structure for natural language modeling
Supertagging: an approach to almost parsing
Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
SPMT: statistical machine translation with syntactified target language phrases
EMNLP '06 Proceedings of the 2006 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
Syntactically Lexicalized Phrase-Based SMT
IEEE Transactions on Audio, Speech, and Language Processing
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A challenging aspect of Statistical Machine Translation from Arabic to English lies in bringing the Arabic source morpho-syntax to bear on the lexical as well as word-order choices of the English target string. In this article, we extend the feature-rich discriminative Direct Translation Model 2 (DTM2) with a novel linear-time parsing algorithm based on an eager, incremental interpretation of Combinatory Categorial Grammar. This way we can reap the benefits of a target syntactic enhancement that leads to more grammatical output while also enabling dynamic decoding without the risk of blowing up decoding space and time requirements. Our model defines a mix of model parameters, some of which involve DTM2 source morpho-syntactic features, and others are novel target side syntactic features. Alongside translation features extracted from the derived parse tree, we explore syntactic features extracted from the incremental derivation process. Our empirical experiments show that our model significantly outperforms the state-of-the-art DTM2 system.