Class-based n-gram models of natural language
Computational Linguistics
A maximum entropy approach to natural language processing
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Decoding complexity in word-replacement translation models
Computational Linguistics
A DP based search using monotone alignments in statistical translation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Example-Based Machine Translation in the Pangloss system
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
The Candide system for machine translation
HLT '94 Proceedings of the workshop on Human Language Technology
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Fast sequential decoding algorithm using a stack
IBM Journal of Research and Development
An algorithmic framework for the decoding problem in statistical machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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Recently statistical methods for natural language translation have become popular and found reasonable success. In this paper we describe an English-Hindi statistical machine translation system. Our machine translation system is based on IBM Models 1, 2, and 3. We present experimental results on an English-Hindi parallel corpus consisting of 150,000 sentence pairs. We propose two new algorithms for the transfer of fertility parameters from Model 2 to Model 3. Our algorithms have a worst case time complexity of O(m3) improving on the exponential time algorithm proposed in the classical paper on IBM Models. When the maximum fertility of a word is small, our algorithms are O(m2) and hence very efficient in practice.