Z-MERT: a fully configurable open source tool for minimum error rate training of machine translation systems. State-of-the-art machine translation (MT) systems rely on several models to evaluate the ”goodness” of a given candidate translation in the target language. Each model would correspond to a feature that is a function of a <candidate translation,foreign sentence> pair. Treated as a log-linear model, we need to assign a weight for each of the features. Och (2003) provides empirical evidence that setting those weights should take into account the evaluation metric by which the MT system will eventually be judged (i.e. maximize performance on a development set, as measured by that evaluation metric). The other insight of Och’s work is that there exists an efficient algorithm to find such weights. This process is known as the MERT phase, for Minimum Error Rate Training. The existence of a MERT module that can be integrated with minimal effort with an existing MT system would be beneficial for the research community. For maximum benefit, this tool should be easy to set up and use and should have a demonstrably efficient implementation. Z-MERT (Zaidan, 2009) is a tool developed with these goals in mind. Great care has been taken to ensure that Z-MERT can be used with any MT system without modification to the code, and without the need for an elaborate web of scripts, which is a situation that unfortunately exists in practice in current training pipelines.
References in zbMATH (referenced in 1 article )
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- Martínez-Gómez, Pascual; Sanchis-Trilles, Germán; Casacuberta, Francisco: Online adaptation strategies for statistical machine translation in post-editing scenarios (2012)