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Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets

arXiv 2104.05501 ↗First posted 12 Apr 2021cs.CL cs.SIcited by 0

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Abstract (arXiv)

We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Mining for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Distill- BERT on each task, as well as first fine-tuning the model on the other task. We explore how much fine-tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 6 references that are in the map. First posted on arXiv 12 Apr 2021.

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References · 3

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  3. 2019To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Taskscited by 167

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