Team DiSaster at SemEval-2020 Task 11: Combining BERT and hand-crafted Features for Identifying Propaganda Techniques in News

Anders Kaas, Viktor Torp Thomsen, Barbara Plank

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

The identification of communication techniques in news articles such as propaganda is important, as such techniques can influence the opinions of large numbers of people. Most work so far focused on the identification at the news article level. Recently, a new dataset and shared task has been proposed for the identification of propaganda techniques at the finer-grained span level. This paper describes our system submission to the subtask of technique classification (TC) for the SemEval 2020 shared task on detection of propaganda techniques in news articles. We propose a method of combining neural BERT representations with hand-crafted features via stacked generalization. Our model has the added advantage that it combines the power of contextual representations from BERT with simple span-based and article-based global features. We present an ablation study which shows that even though BERT representations are very powerful also for this task, BERT still benefits from being combined with carefully designed task-specific features.
Original languageEnglish
Title of host publicationSemEval 2020
PublisherAssociation for Computational Linguistics
Publication date2020
Publication statusPublished - 2020

Keywords

  • Propaganda detection
  • Span-level classification
  • Neural BERT representations
  • Hand-crafted features
  • Stacked generalization

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