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
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A word-to-phrase statistical translation model
ACM Transactions on Speech and Language Processing (TSLP)
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
Simultaneous translation of lectures and speeches
Machine Translation
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For several reasons machine translation systems are today unsuited to process long texts in one shot. In particular, in statistical machine translation, heuristic search algorithms are employed whose level of approximation depends on the length of the input. Moreover, processing time can be a bottleneck with long sentences, whereas multiple text chunks can be quickly processed in parallel. Hence, in real working conditions the problem arises of how to optimally split the input text. In this work, we investigate several text segmentation criteria and verify their impact on translation performance by means of a statistical phrase-based translation system. Experiments are reported on a popular as well as difficult task, namely the translation of news agencies from Chinese-English as proposed by the NIST MT evaluation workshops. Results reveal that best performance can be achieved by taking into account both linguistic and input length constraints.