%Aigaion2 BibTeX export from Idiap Publications
%Friday 05 December 2025 06:28:20 AM

@INPROCEEDINGS{VILLATORO-TELLO_IBERIANLANGUAGESEVALUATIONFORUM(IBERLEF2020)_2020,
                      author = {Villatoro-Tello, Esa{\'{u}} and Ram{\'{\i}}rez-de-la-Rosa, Gabriela and Parida, Shantipriya and Kumar, Sajit and Motlicek, Petr},
                    keywords = {deep learning, Natural language processing, Supervised Autoencoders, Text Representation},
                    projects = {Idiap, Innosuisse-SM2, EC H2020-ROXANNE},
                       month = sep,
                       title = {Idiap and UAM Participation at MEX-A3T Evaluation Campaign},
                   booktitle = {Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020) co-located with 36th Conference of the Spanish Society for Natural Language Processing (SEPLN 2020)},
                      volume = {2664},
                        year = {2020},
                       pages = {6},
                   publisher = {CEUR Workshop Proceedings},
                        issn = {1613-0073},
                         url = {http://ceur-ws.org/Vol-2664/mexa3t_paper3.pdf},
                    abstract = {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 (},
                         pdf = {https://publications.idiap.ch/attachments/papers/2021/VILLATORO-TELLO_IBERIANLANGUAGESEVALUATIONFORUM(IBERLEF2020)_2020.pdf}
}