NLP North at WNUT-2020 Task 2: Pre-training versus Ensembling for Detection of Informative COVID-19 English Tweets
Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review
With the COVID-19 pandemic raging world-wide since the beginning of the 2020 decade,the need for monitoring systems to track relevant information on social media is vitally important. This paper describes our submission to the WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. We investigate the effectiveness for a variety of classification models, and found that domain-specific pre-trained BERT models lead to the best performance. On top of this, we attempt a variety of ensembling strategies, but these at-tempts did not lead to further improvements.Our final best model, the standalone CT-BERT model, proved to be highly competitive, leading to a shared first place in the shared task.Our results emphasize the importance of do-main and task-related pre-training.
|Title of host publication||Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)|
|Publisher||Association for Computational Linguistics|
|Publication date||Nov 2020|
|Publication status||Published - Nov 2020|