Adolfo Hernandez

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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)
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)
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)
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)
ii Preface Recent initiatives in language technology have led to the development of at least minimal language processing kits for all official European languages. This is a big step towards automatic processing and/or extraction of information especially from official documents produced within the European Union. Apart from those official languages, a large(More)
Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in which risks have to be estimated. The uncertainty of classification is determined by a trade-off between the amount of data available for training,(More)