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ur-iw-hnt at GermEval 2021: An Ensembling Strategy with Multiple BERT Models

arXiv 2110.02042 ↗First posted 5 Oct 2021cs.CL cs.AIcited by 0

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Abstract (arXiv)

This paper describes our approach (ur-iw-hnt) for the Shared Task of GermEval2021 to identify toxic, engaging, and fact-claiming comments. We submitted three runs using an ensembling strategy by majority (hard) voting with multiple different BERT models of three different types: German-based, Twitter-based, and multilingual models. All ensemble models outperform single models, while BERTweet is the winner of all individual models in every subtask. Twitter-based models perform better than GermanBERT models, and multilingual models perform worse but by a small margin.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 64 references that are in the map. First posted on arXiv 5 Oct 2021.

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References · 20

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8 more
  1. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  2. 2020German's Next Language Modelcited by 144
  3. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
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  7. 2020Which *BERT? A Survey Organizing Contextualized Encoderscited by 26
  8. 2020Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussionscited by 5

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