%Aigaion2 BibTeX export from Idiap Publications
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@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}
}