"This sentence is wrong." Detecting errors in machine-translated sentences.

International audienceMachine translation systems are not reliable enough to be used ''as is'': except for the most simple tasks, they can only be used to grasp the general meaning of a text or assist human translators. The purpose of confidence measures is to detect erroneous words or sentences produced by a machine translation system. In this article after reviewing the mathematical foundations of confidence estimation we propose a comparison of several state-of-the-art confidence measures, predictive parameters and classifiers. We also propose two original confidence measures based on Mutual Information and a method for automatically generating data for training and testing classifiers. We applied these techniques to data from WMT campaign 2008 and found that the best confidence measures yielded an Equal Error Rate of 36.3% at word level and 34.2% at sentence level, but combining different measures reduced these rates to respectively 35.0\% and 29.0\%. We also present the results of an experiment aimed at determining how helpful confidence measures are in a post edition task. Preliminary results suggest that our system is not yet ready to efficiently help post editors, but we now have a software and protocol we can apply to further experiments, and user feedback has indicated aspects which must be improved in order to increase the level of helpfulness of confidence measures

Constrained domain maximum likelihood estimation and the loss function in statistical pattern recognition

In this thesis we present a new estimation algorithm for statistical models which does not incurs in the over-trainning problems. This new estimation techinque, the so-called, constrained domain maximum likelihood estimation (CDMLE) holds all the theoretical properties of the maximum likelihood estimation and furthermore it does not provides overtrained parameter sets. On the other hand, the impliations of the the 0-1 loss function assumption are analysed in the pattern recognition tasks. Specifically, more versatile functions are designed without increasing the optimal classification rule costs. This approach is applied to the statistical machine translation problem.Andrés Ferrer, J. (2008). Constrained domain maximum likelihood estimation and the loss function in statistical pattern recognition. http://hdl.handle.net/10251/13638Archivo delegad