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

arXiv: https://arxiv.org/abs/2010.05327 · HTML: https://mlsanity.com/paper/2010.05327/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [Comparing Approaches to Dravidian Language Identification](https://mlsanity.com/paper/2103.05552/) (2021)
- [WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans](https://mlsanity.com/paper/2104.04630/) (2021)
- [Transformers to Fight the COVID-19 Infodemic](https://mlsanity.com/paper/2104.12201/) (2021)
- [Language Identification of Hindi-English tweets using code-mixed BERT](https://mlsanity.com/paper/2107.01202/) (2021)
- [DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain](https://mlsanity.com/paper/2205.06025/) (2022)
- [TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation](https://mlsanity.com/paper/2104.04632/) (2021)
- [WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments](https://mlsanity.com/paper/2108.00057/) (2021)

## 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.