A systematic comparison of various statistical alignment models
Computational Linguistics
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Alternating quantifier scope in CCG
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Discriminative training and maximum entropy models for statistical 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
Learning non-isomorphic tree mappings for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
A phrase-based, joint probability model for statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Statistical machine translation by parsing
ACL '04 Proceedings of the 42nd 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
N-gram-based Machine Translation
Computational Linguistics
Improving statistical MT by coupling reordering and decoding
Machine Translation
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
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
Arabic preprocessing schemes for statistical machine translation
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Morpho-syntactic information for automatic error analysis of statistical machine translation output
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
Comparing phrase-based and syntax-based paraphrase generation
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Syntactic dependency-based n-grams: more evidence of usefulness in classification
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Syntactic dependency-based n-grams as classification features
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Syntactic N-grams as machine learning features for natural language processing
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
In this paper we compare and contrast two approaches to Machine Translation (MT): the CMU-UKA Syntax Augmented Machine Translation system (SAMT) and UPC-TALP N-gram-based Statistical Machine Translation (SMT). SAMT is a hierarchical syntax-driven translation system underlain by a phrase-based model and a target part parse tree. In N-gram-based SMT, the translation process is based on bilingual units related to word-to-word alignment and statistical modeling of the bilingual context following a maximum-entropy framework. We provide a step-by-step comparison of the systems and report results in terms of automatic evaluation metrics and required computational resources for a smaller Arabic-to-English translation task (1.5M tokens in the training corpus). Human error analysis clarifies advantages and disadvantages of the systems under consideration. Finally, we combine the output of both systems to yield significant improvements in translation quality.