Leveraging Event Specific and Chunk Span features to Extract COVID Events from tweets

TL;DR

This paper presents a system that effectively extracts COVID-19 related events from Twitter data using specialized features and multi-task learning, achieving top performance in a shared task competition.

Contribution

The authors introduce a novel approach combining event-specific and chunk span features with COVID-Twitter-Bert for improved event extraction from tweets.

Findings

Ranked 1st in WNUT 2020 Shared Task-3 with F1 of 0.6598

Utilized multi-task models for slot-filling and sentence classification

Achieved high accuracy without ensembles or extra datasets

Abstract

Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection. The system consists of separate multi-task models for slot-filling subtasks and sentence-classification subtasks while leveraging the useful sentence-level information for the corresponding event. The system uses COVID-Twitter-Bert with attention-weighted pooling of candidate slot-chunk features to capture the useful information chunks. The system ranks 1st at the leader-board with F1 of 0.6598, without using any ensembles or additional datasets. The code and trained models are available at this https URL.