Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup

Jishnu Ray Chowdhury, Cornelia Caragea, Doina Caragea


Abstract
Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management. For this purpose, we compile a multilingual dataset of over 130K samples for multi-label classification of disaster-related tweets. We present a masking-based loss function for partially labelled samples and demonstrate the effectiveness of Manifold Mixup in the text domain. Our main model is based on Multilingual BERT, which we further improve with Manifold Mixup. We show that our model generalizes to unseen disasters in the test set. Furthermore, we analyze the capability of our model for zero-shot generalization to new languages. Our code, dataset, and other resources are available on Github.
Anthology ID:
2020.acl-srw.39
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
292–298
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-srw.39
DOI:
10.18653/v1/2020.acl-srw.39
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-srw.39.pdf
Video:
 http://slideslive.com/38928661