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Zampieri et al: SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) [5b03f3ab]

Tags: papers, nlp, bias nlp project, arabic nlp

1. Summary

  • Set up a set of classification tasks for offensive language across multiple languages

    1. Identification of offensive languages (y/n on whether something is offensive)

    2. Automatic categorization of offensive types (targeted or not targeted?)

      • targeted - insults towards a group or individual
      • not targeted - profanity or swearing

    3. Target identification (invidual, group, other)

      • other category includes organizations, events, issues
  • Used the Offensive Language Identification Dataset (OLID), a 14k+ english tweets with the three types of identification labeled\
  • Teams were provided OLID and SOLID (Semi-supervised offensive language identification dataset)

  • Language breakdown

    • Arabic dataset had 10k tweets (twitter langauge filter on lang:ar)
    • Danish had 3.6k comments from FB, Reddit, and local newspaper
    • Greek used the Offensive Greek Twitter Dataset, 10k+ tweets
    • Turkish used 35k+ tweets

  • Models

    • Most teams used contextualized transformers (Bert/Roberta/mBert)
    • Wordembeddings also showed up, some sentiment analysis as well

  • Results

    • Best performing team used a cross language model (XML-Roberta), emsembled with XML-Rob base and large