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Transformer-based Multi-task Learning for Disaster Tweet Categorisation
Congcong Wang
Paul Nulty
David Lillis
Anouck Adrot
Rob Grace
Kathleen Moore
Christopher W. Zobel
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.
openurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fidl.iscram.org%2F&genre=proceeding&title=Transformer-based%20Multi-task%20Learning%20for%20Disaster%20Tweet%20Categorisation&stitle=Iscram%202021&issn=978-1-949373-61-5&date=2021&spage=705&epage=718&aulast=Congcong%20Wang&au=Paul%20Nulty&au=David%20Lillis&pub=Virginia%20Tech&place=Blacksburg%2C%20VA%20%28USA%29&sid=refbase%3AISCRAM
url:http://idl.iscram.org/show.php?record=2366
citekey:CongcongWang_etal2021
citation:Congcong Wang, Paul Nulty, & David Lillis. (2021). Transformer-based Multi-task Learning for Disaster Tweet Categorisation. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 705-718). Blacksburg, VA (USA): Virginia Tech.
2021
ConferencePaper
text
Disaster Response, Tweet Analysis, Transformers, Natural Language Processing
file:http://idl.iscram.org/files/congcongwang/2021/2366_CongcongWang_etal2021.pdf
Virginia Tech
English
978-1-949373-61-5
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management
2021
705
718
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