mlsanityData to 7 Oct 2026

InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction

arXiv 2010.05327 ↗First posted 11 Oct 2020cs.CL cs.LGcited by 9

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Abstract (arXiv)

Identifying informative tweets is an important step when building information extraction systems based on social media. WNUT-2020 Task 2 was organised to recognise informative tweets from noise tweets. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves 10th place in the final rankings scoring 0.9004 F1 score for the test set.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Oct 2020.

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Citing papers per year: 2021: 8; 2022: 1.

9 papers.

  1. 2021MUDES: Multilingual Detection of Offensive Spanscited by 30
  2. 2021Multilingual Offensive Language Identification for Low-resource Languagescited by 22
  3. 2021Comparing Approaches to Dravidian Language Identificationcited by 11
  4. 2021WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spanscited by 6
  5. 2021Transformers to Fight the COVID-19 Infodemiccited by 4
  6. 2021Language Identification of Hindi-English tweets using code-mixed BERTcited by 4
  7. 2022DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domaincited by 4
  8. 2021TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguationcited by 3
  9. 2021WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Commentscited by 3