Machine Translation: its history, current status, and future prospects
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
A comparison of alignment models for statistical machine translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
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
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Clause restructuring for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Machine translation using probabilistic synchronous dependency insertion grammars
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Improving a statistical MT system with automatically learned rewrite patterns
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A path-based transfer model for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Stat-XFER: a general search-based syntax-driven framework for machine translation
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
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We describe the syntactic structure transfer, a central design question in machine translation, between two languages Tamil (source) and Hindi (target), belonging to two different language families, Dravidian and Indo-Aryan respectively. Tamil and Hindi differ extensively at the clausal construction level and transferring the structure is difficult. The syntactic structure transfer described here is a hybrid approach where we use CRFs for identifying the clause boundaries in the source language, Transformation Based Learning (TBL) for extracting the rules and use semantic classification of Postpositions (PSP) for choosing semantically appropriate structure in constructions where there are one to many mapping in the target language. We have evaluated the system using web data and the results are encouraging.