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This paper describes the UPC participation in the WMT 12 evaluation campaign. All systems presented are based on standard phrase-based Moses systems. Variations adopted several improvement techniques such as morphology simplification and generation and domain adaptation. The morphology simplification overcomes the data sparsity problem when translating into(More)
The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models(More)
This paper gives a description of the statistical machine translation (SMT) systems developed at the TALP Research Center of the UPC (Universitat Politècnica de Catalunya) for our participation in the IWSLT'08 evaluation campaign. We present N gram-based (TALPtuples) and phrase-based (TALPphrases) SMT systems. The paper explains the 2008 systems'(More)
Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the classifier diversity and the required performance. The interpretability of MCSs(More)
Bayesian averaging (BA) over ensembles of decision models allows evaluation of the uncertainty of decisions that is of crucial importance for safety-critical applications such as medical diagnostics. The interpretability of the ensemble can also give useful information for experts responsible for making reliable decisions. For this reason, decision trees(More)
This work aims to improve an N-gram-based statistical machine translation system between the Catalan and Spanish languages, trained with an aligned Spanish– Catalan parallel corpus consisting of 1.7 million sentences taken from El Periódico newspaper. Starting from a linguistic error analysis above this baseline system, orthographic, morphological, lexical,(More)
In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an(More)
  • Cody Anthony Hernandez, San Marcos, Kevin Lewis, Heather C, Galloway, Dean +12 others
  • 2015
section 107). Consistent with fair use as defined in the Copyright Laws, brief quotations from this material are allowed with proper acknowledgment. Use of this material for financial gain without the author's express written permission is not allowed. his lectures over related topics, training in scientific writing, and mentorship through this project. I(More)