CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

Abstract

This paper presents our submission to Task 2 of the Workshop on Noisy User-generated Text. We explore improving the performance of a pre-trained transformer-based language model fine-tuned for text classification through an ensemble implementation that makes use of corpus level information and a handcrafted feature. We test the effectiveness of including the aforementioned features in accommodating the challenges of a noisy data set centred on a specific subject outside the remit of the pre-training data. We show that inclusion of additional features can improve classification results and achieve a score within 2 points of the top performing team.
Original languageEnglish
Title of host publicationProceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Place of PublicationOnline
PublisherAssociation for Computational Linguistics
Pages352-358
Number of pages7
DOIs
Publication statusPublished - 1 Nov 2020

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