David Lillis: Crisis Domain Adaptation Using Sequence-to-Sequence Transformers

Crisis Domain Adaptation Using Sequence-to-Sequence Transformers

Congcong Wang, Paul Nulty and David Lillis

In A. Adrot, R. Grace, K. Moore, and C. W. Zobel, editors, ISCRAM 2021 Conference Proceedings -- 18th International Conference on Information Systems for Crisis Response and Management, pages 655--666, Blacksburg, VA (USA), 2021. Virginia Tech.


User-generated content (UGC) on social media can act as a key source of information for emergency responders incrisis situations. However, due to the volume concerned, computational techniques are needed to effectively filter and prioritise this content as it arises during emerging events. In the literature, these techniques are trained using annotated content from previous crises. In this paper, we investigate how this prior knowledge can be best leveraged for new crises by examining the extent to which crisis events of a similar type are more suitable for adaptation tonew events (cross-domain adaptation). Given the recent successes of transformers in various language processing tasks, we propose CAST: an approach for Crisis domain Adaptation leveraging Sequence-to-sequence Transformers. We evaluate CAST using two major crisis-related message classification datasets. Our experiments show that ourCAST-based best run without using any target data achieves the state of the art performance in both in-domain and cross-domain contexts. Moreover, CAST is particularly effective in one-to-one cross-domain adaptation when trained with a larger language model. In many-to-one adaptation where multiple crises are jointly used as the source domain, CAST further improves its performance. In addition, we find that more similar events are more likely to bring better adaptation performance whereas fine-tuning using dissimilar events does not help for adaptation. To aid reproducibility, we open source our code to the community.