Improved Minimum Error Rate Training in Moses
نویسندگان
چکیده
منابع مشابه
Improved Minimum Error Rate Training in Moses
We describe an open-source implementation of minimum error rate training (MERT) for statistical machine translation (SMT). This was implemented within the Moses toolkit, although it is essentially standsalone, with the aim of replacing the existing implementation with a cleaner, more flexible design, in order to facilitate further research in weight optimisation. A description of the design is ...
متن کاملMinimum Error Rate Training Semiring
Modern Statistical Machine Translation (SMT) systems make their decisions based on multiple information sources, which assess various aspects of the match between a source sentence and its possible translation(s). Tuning a SMT system consists in finding the right balance between these sources so as to produce the best possible output, and is usually achieved through Minimum Error Rate Training ...
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The most commonly used method for training feature weights in statistical machine translation (SMT) systems is Och’s minimum error rate training (MERT) procedure. A well-known problemwith Och’s procedure is that it tends to be sensitive to small changes in the system, particularly when the number of features is large. In this paper, we quantify the stability of Och’s procedure by supplying diff...
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Minimum Error Rate Training (MERT) remains one of the preferred methods for tuning linear parameters in machine translation systems, yet it faces significant issues. First, MERT is an unregularized learner and is therefore prone to overfitting. Second, it is commonly used on a noisy, non-convex loss function that becomes more difficult to optimize as the number of parameters increases. To addre...
متن کاملOptimal Search for Minimum Error Rate Training
Minimum error rate training is a crucial component to many state-of-the-art NLP applications, such as machine translation and speech recognition. However, common evaluation functions such as BLEU or word error rate are generally highly non-convex and thus prone to search errors. In this paper, we present LP-MERT, an exact search algorithm for minimum error rate training that reaches the global ...
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ژورنال
عنوان ژورنال: The Prague Bulletin of Mathematical Linguistics
سال: 2009
ISSN: 1804-0462,0032-6585
DOI: 10.2478/v10108-009-0011-9