%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{VILLATORO-TELLO_5THSWISSTEXT&16THKONVENSJOINTCONFERENCE2020_2020,
         author = {VILLATORO-TELLO, Esa{\'{u}} and Parida, Shantipriya and Kumar, Sajit and Motlicek, Petr and Zhan, Qingran},
       projects = {Idiap, Innosuisse-SM2, EC H2020-ROXANNE},
          month = jun,
          title = {Idiap & UAM participation at GermEval 2020: Classification and Regression of Cognitive and Motivational Style from Text},
      booktitle = {Proceedings of the GermEval 2020 Shared Task on the Classification and Regression of Cognitive and Motivational style from Text},
           year = {2020},
            url = {https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2020-cognitive-motive/ge20st1-paper-2.pdf},
       abstract = {In this paper, we describe the participation of the Idiap Research Institute at GermEval 2020 shared task on the Classification and Regression of Cognitive and Motivational style from Text, specifically on subtask 2, Classification of the Operant Motive Test (OMT). Generally speaking, GermEval 2020 aims at encouraging the Natural Language Understanding (NLU) research community in proposing novel methodologies for assessing the connection between freely written texts and its cognitive and motivational styles. For evaluating this task, organizers provided a large dataset containing textual descriptions, in German language,  generated by more than 14,000 participants. Our participation aims at evaluating the impact of advanced language representation, e.g., Bert, XLM, and DistilBERT in combination with some traditional machine learning algorithms. Our best configuration was able to obtain an F1 macro of 69.8\% on the test partition, which represents a relative improvement of 7.4\% in comparison to the proposed baseline.},
            pdf = {https://publications.idiap.ch/attachments/papers/2020/VILLATORO-TELLO_5THSWISSTEXT&16THKONVENSJOINTCONFERENCE2020_2020.pdf}
}