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文章基本信息

  • 标题:NoNLPTask Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems
  • 本地全文:下载
  • 作者:Myrthe Reuver ; Antske Fokkens ; Suzan Verberne
  • 期刊名称:Conference on European Chapter of the Association for Computational Linguistics (EACL)
  • 出版年度:2021
  • 卷号:2021
  • 页码:45-55
  • 语种:English
  • 出版社:ACL Anthology
  • 摘要:Natural Language Processing (NLP) is defined by specific, separate tasks, with each their own literature, benchmark datasets, and definitions. In this position paper, we argue that for a complex problem such as the threat to democracy by non-diverse news recommender systems, it is important to take into account a higher-order, normative goal and its implications. Experts in ethics, political science and media studies have suggested that news recommendation systems could be used to support a deliberative democracy. We reflect on the role of NLP in recommendation systems with this specific goal in mind and show that this theory of democracy helps to identify which NLP tasks and techniques can support this goal, and what work still needs to be done. This leads to recommendations for NLP researchers working on this specific problem as well as researchers working on other complex multidisciplinary problems.
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