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         author = {Vallejo-Aldana, Daniel and L{\'{o}}pez-Monroy, A. Pastor and VILLATORO-TELLO, Esa{\'{u}}},
       projects = {Idiap},
          month = sep,
          title = {Leveraging Events Sub-Categories for Violent-Events Detection in Social Media},
      booktitle = {Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022)},
         volume = {3202},
           year = {2022},
            url = {http://ceur-ws.org/Vol-3202/davincis-paper3.pdf},
       abstract = {This paper describes our participation in the shared evaluation campaign of DA-VINCIS@IberLEF 2022. In this work, we addressed the Violent Event Identification (VEI) task by exploiting Bidirectional Encoder Representations from Transformers (BERT) in combination with Multi-Task learning approaches.
Our results indicate that the proposed architecture is able to leverage information about the crime categories for effectively detect the mention of a violent act within a tweet.  Our approach obtained the best performance (F1=0.77) among 11 different teams and a total of 32 different submissions.},
            pdf = {https://publications.idiap.ch/attachments/papers/2022/Vallejo-Aldana_IBERLEF2022_2022.pdf}