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Author
Congcong Wang
;
Paul Nulty
;
David Lillis
Title
Transformer-based Multi-task Learning for Disaster Tweet Categorisation
Type
Conference Article
Year
2021
Publication
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management
Abbreviated Journal
Iscram 2021
Volume
Issue
Pages
705-718
Keywords
Disaster Response, Tweet Analysis, Transformers, Natural Language Processing
Abstract
Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can contribute to the situational awareness of emergency responders, who have a need for them to be categorised according to information types (i.e. the type of aid services the messages are requesting). We introduce a transformer-based multi-task learning (MTL) technique for classifying information types and estimating the priority of these messages. We evaluate the effectiveness of our approach with a variety of metrics by submitting runs to the TREC Incident Streams (IS) track: a research initiative specifically designed for disaster tweet classification and prioritisation. The results demonstrate that our approach achieves competitive performance in most metrics as compared to other participating runs. Subsequently, we find that an ensemble approach combining disparate transformer encoders within our approach helps to improve the overall effectiveness to a significant extent, achieving state-of-the-art performance in almost every metric. We make the code publicly available so that our work can be reproduced and used as a baseline for the community for future work in this domain.
Address
University College Dublin; University College Dublin; University College Dublin
Corporate Author
Thesis
Publisher
Virginia Tech
Place of Publication
Blacksburg, VA (USA)
Editor
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language
English
Summary Language
English
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
978-1-949373-61-5
ISBN
Medium
Track
Social Media for Disaster Response and Resilience
Expedition
Conference
18th International Conference on Information Systems for Crisis Response and Management
Notes
wangcongcongcc@gmail.com
Approved
no
Call Number
ISCRAM @ idladmin @
Serial
2366
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