mlsanityData to 7 Oct 2026

WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments

arXiv 2108.00057 ↗First posted 30 Jul 2021cs.CL cs.AI cs.NEcited by 3

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Abstract (arXiv)

This paper addresses the identification of toxic, engaging, and fact-claiming comments on social media. We used the dataset made available by the organizers of the GermEval-2021 shared task containing over 3,000 manually annotated Facebook comments in German. Considering the relatedness of the three tasks, we approached the problem using large pre-trained transformer models and multitask learning. Our results indicate that multitask learning achieves performance superior to the more common single task learning approach in all three tasks. We submit our best systems to GermEval-2021 under the team name WLV-RIT.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 49 references that are in the map. First posted on arXiv 30 Jul 2021.

Builds on

22 of 49 references.

References · 22

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10 more
  1. 2020Arabic Offensive Language on Twitter: Analysis and Experimentscited by 28
  2. 2021Ruddit: Norms of Offensiveness for English Reddit Commentscited by 26
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  4. 2021Multilingual Offensive Language Identification for Low-resource Languagescited by 22
  5. 2021An Exploratory Analysis of the Relation Between Offensive Language and Mental Healthcited by 14
  6. 2020Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detectioncited by 12
  7. 2021Comparing Approaches to Dravidian Language Identificationcited by 11
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  9. 2020InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extractioncited by 9
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Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.

  1. 2023Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detectioncited by 10
  2. 2022BERT(s) to Detect Multiword Expressionscited by 5
  3. 2023Can Model Fusing Help Transformers in Long Document Classification? An Empirical Studycited by 3