TEST _ POSITIVE at W - NUT 2020 Shared Task-3: Cross-task modeling (2020)

Abstract: The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro F1.

## introduction in this work, we report the system architecture and results of the team test positive in the competition of w-nut 2020 sharred task-3: extracting covid-19 event from twitter. since february 2020, the pandemic covid-19 has been spreading all over the world, posing a significant threat to mankind in every aspect. the information sharing about a pandemic has been critical in stopping virus spreading. with the recent advance of social networks and machine learning, we are able to automatically detect potential events of covid cases, and identify key information to prepare ahead. we are interested in covid-19 related event extraction from tweets. with the prevalence of coronavirus, twitter has been a valuable source of news and information. twitter users share covid-19 related topics about personal narratives and news on social media @xcite . the information could be helpful for doctors, epidemiologists, and policymakers in controlling the pandemic. however, manual extracting useful information from tremendous amount of tweets is impossible. hence, we aim to develop a system to automatically extract structured knowledge from twitter. extracting covid-19 related events from twitter is non-trivial due to the following challenges: ## related work event extraction from twitter impressive efforts have been made to detect events from twitter. existing works include domain specific event extraction and open domain event extraction. for domain specific extraction, approaches mainly focus on extracting a particular type of events, including natural disasters @xcite , traffic events @xcite , user mobility behaviors @xcite , and etc. the open domain scenario is more challenging and usually relies on unsupervised approaches. existing works usually create clusters with event-related keywords @xcite , or named entities @xcite . additionally, @xcite and @xcite design general pipelines to extract and categorize events in supervised and unsupervised manner respectively. different from previous works, we deal with covid-19 related event extraction in particular. zong et al. (2020) ## method in this section, we introduce our approach joelin and its data pre-processing and post-processing steps in detail. first, we pre-process the noisy twitter data following the data cleaning procedures in @xcite . second, we train joelin and fine-tune the pre-trained language model endto-end. specifically, we design the joelin classifier in a joint event multi-task learning framework. moreover, we provide four options of embedding types and ensemble the outputs with the highest validation score. finally, we further utilize ner techniques to post-process our results with minimum hand-crafted rules. ## conclusion in this work, we build joelin upon a joint event multi-task learning framework. we use ner-based