On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
THE WEIGHTED MAJORITY ALGORITHM (Supersedes 89-16)
THE WEIGHTED MAJORITY ALGORITHM (Supersedes 89-16)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
L1 regularized regression for reranking and system combination in machine translation
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
RegMT system for machine translation, system combination, and evaluation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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We analyze adaptive model weighting techniques for reranking using instance scores obtained by L1 regularized transductive regression. Competitive statistical machine translation is an on-line learning technique for sequential translation tasks where we try to select the best among competing statistical machine translators. The competitive predictor assigns a probability per model weighted by the sequential performance. We define additive, multiplicative, and loss-based weight updates with exponential loss functions for competitive statistical machine translation. Without any pre-knowledge of the performance of the translation models, we succeed in achieving the performance of the best model in all systems and surpass their performance in most of the language pairs we considered.