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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
Peer-review

Open access

Publication Information

Output type

Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 331-336

Publication milestones

  • Published - 11/2020

Publication status

Published - 11/2020

Publisher

Association for Computational Linguistics, United States

Host publication title

Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

Abstract

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.

Access to documents

Related Event

Title

The Sixth Workshop on Noisy User-generated Text

Event type

Conference

Date

19/11/2020 - 19/11/2020

Location

Online