Idiap and UAM Participation at MEX-A3T Evaluation Campaign

This paper describes our participation in the shared evaluation campaign of MexA3T 2020. Our main goal wasto evaluate a Supervised Autoencoder (SAE) learning algorithm in text classification tasks. For our experiments,we used three different sets of features as inputs, namely classic word n-grams, char n-grams, and Spanish BERTencodings. Our results indicate that SAE is adequate for longer and more formal written texts. Accordingly,our approach obtained the best performance (