# Multi-task transfer learning for finding actionable information from crisis-related messages on social media

arXiv: https://arxiv.org/abs/2102.13395 · HTML: https://mlsanity.com/paper/2102.13395/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Processing Social Media Messages in Mass Emergency: A Survey](https://mlsanity.com/paper/1407.7071/) (2014)
- [Domain Adaptation with Adversarial Training and Graph Embeddings](https://mlsanity.com/paper/1805.05151/) (2018)
- [CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding](https://mlsanity.com/paper/2005.06627/) (2020)
- [UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19 Event Extraction on Social Media](https://mlsanity.com/paper/2009.10047/) (2020)

## Built on by (most cited first, 2 of 2)

- [Crisis Domain Adaptation Using Sequence-to-sequence Transformers](https://mlsanity.com/paper/2110.08015/) (2021)
- [UCD-CS at TREC 2021 Incident Streams Track](https://mlsanity.com/paper/2112.03737/) (2021)

## Abstract (arXiv)

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.