We present a method to use multilayer perceptrons (MLPs) for a verification task, i.e. to verify whether two vectors are from the same class or not. In tests with synthetic data we could show that the verification MLPs are almost optimal from a Bayesian point of view. With speech data we have shown that verification MLPs generalize well such that they can be deployed as well for classes which w...

The purpose of this work is to investigate the use of machine learning approaches for confidence estimation within a statistical machine translation application. Specifically, we attempt to learn probabilities of correctness for various model predictions, based on the native probabilites (i.e. the probabilites given by the original model) and on features of the current context. Our experiments ...

In this paper we present an approach to the application of specific models to the understanding process of a dialogue system. The previous classification process is done by means of Multilayer Perceptrons, and Hidden Markov Models are used for the semantic modeling. The task consists of answering telephone queries about train timetables, prices and services for long distance trains in Spanish. ...

In this paper we present an approach to the application of specific models to the understanding process of a dialogue system. The previous classification process is done by means of Multilayer Perceptrons, and Hidden Markov Models are used for the semantic modeling. The task consists of answering telephone queries about train timetables, prices and services for long distance trains in Spanish. ...

This paper describes the participation of ELiRF-UPV team at Classification Of Spanish Election Tweets (COSET) task. We tested several approaches based on different classifiers and features representations. Our main approach is based on neural networks, concretely, Multilayer Perceptrons (MLP) with bag-of-words representation of the tweets. Our system achieved the best score on the test set of t...

Using neural networks, the present study replicates previous results on prediction of student dropout obtained with decision trees and logistic regressions. For this purpose, multilayer perceptrons are trained same data as in initial study. It is shown that networks lead to a significant improvement students at risk. Already after first semester, potential dropouts can be identified probability...

A novel framework for audio-assisted dialogue detection based on indicator functions and neural networks is investigated. An indicator function defines that an actor is present at a particular time instant. The cross-correlation function of a pair of indicator functions and the magnitude of the corresponding cross-power spectral density are fed as input to neural networks for dialogue detection...