Multi-Task Learning for Detection of Aggressive and Violent Incidents from Social Media

Hoang Thang Ta, Abu Bakar Siddiqur Rahman, Lotfollah Najjar, Alexander Gelbukh

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

In this paper, we participate in the task of Detection of Aggressive and Violent INCIdents from Social Media in Spanish (DA-VINCIS). We apply a multi-task learning network, MT-DNN to train users' tweets on their text embeddings from pre-trained transformer models. In the first subtask, we obtained the best F1 of 74.80%, Precision of 75.52%, and Recall of 74.09%. Meanwhile, F1 of 39.20%, Precision of 37.79%, and Recall of 43.88% are results in the second subtask.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume3202
StatePublished - 2022
Event2022 Iberian Languages Evaluation Forum, IberLEF 2022 - A Coruna, Spain
Duration: 20 Sep 2022 → …

Keywords

  • DA-VINCIS
  • IberLEF
  • MT-DNN
  • Multi-task learning
  • Text Classification
  • Violence Detection

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