Detecting emergency events on social media could facilitate disaster monitoring by categorizing and prioritizing tweets in catastrophic situations to assist emergency service operators. However, the high noise levels in tweets, combined with the limited publicly available datasets have rendered the task difficult. In this paper, we propose an enhanced multitask Transformer-based model that highlights the importance of entities, event descriptions, and hashtags in tweets. This approach includes a Transformer encoder with several layers over the sequential token representation provided by a pre-trained language model that acts as a task adapter for detecting emergency events in noisy data. We conduct an evaluation on the Text REtrieval Conference (TREC) 2021 Incident Streams (IS) track dataset, and we conclude that our proposed approach brought considerable improvements to emergency social media classification.