  The Incident streams (IS) track is a research challenge aimed at finding
important information from social media during crises for emergency response
purposes. More specifically, given a stream of crisis-related tweets, the IS
challenge asks a participating system to 1) classify what the types of users'
concerns or needs are expressed in each tweet, known as the information type
(IT) classification task and 2) estimate how critical each tweet is with regard
to emergency response, known as the priority level prediction task. In this
paper, we describe our multi-task transfer learning approach for this
challenge. Our approach leverages state-of-the-art transformer models including
both encoder-based models such as BERT and a sequence-to-sequence based T5 for
joint transfer learning on the two tasks. Based on this approach, we submitted
several runs to the track. The returned evaluation results show that our runs
substantially outperform other participating runs in both IT classification and
priority level prediction.
