The shared real-time information about natural disasters on social media
platforms like Twitter and Facebook plays a critical role in informing
volunteers, emergency managers, and response organizations. However, supervised
learning models for monitoring disaster events require large amounts of
annotated data, making them unrealistic for real-time use in disaster events.
To address this challenge, we present a fine-grained disaster tweet
classification model under the semi-supervised, few-shot learning setting where
only a small number of annotated data is required. Our model, CrisisMatch,
effectively classifies tweets into fine-grained classes of interest using few
labeled data and large amounts of unlabeled data, mimicking the early stage of
a disaster. Through integrating effective semi-supervised learning ideas and
incorporating TextMixUp, CrisisMatch achieves performance improvement on two
disaster datasets of 11.2\% on average. Further analyses are also provided for
the influence of the number of labeled data and out-of-domain results.

社交媒体平台上关于自然灾害的实时信息在通知志愿者、应急管理人员和救援组织方面起到关键作用。然而，监测灾害事件的监督学习模型需要大量标注数据，使得其在实时灾害事件中难以实现。为了应对这一挑战，我们提出了一种在半监督少样本学习条件下的细粒度灾害推文分类模型，仅需小量标注数据。我们的模型 CrisisMatch 利用少量标注数据和大量无标注数据有效地将推文分类为感兴趣的细粒度类别，模拟灾害的早期阶段。通过集成有效的半监督学习方法和引入 TextMixUp，CrisisMatch 在两个灾害数据集上平均提升了 11.2％ 的性能。此外，我们还对标注数据数量和领域外结果的影响进行了进一步分析。

危机匹配：用于细粒度灾害推文分类的半监督少样本学习

CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster  Tweet Classification

Social media has enabled people to circulate information in a timely fashion,
thus motivating people to post messages seeking help during crisis situations.
These messages can contribute to the situational awareness of emergency
responders, who have a need for them to be categorised according to information
types (i.e. the type of aid services the messages are requesting). We introduce
a transformer-based multi-task learning (MTL) technique for classifying
information types and estimating the priority of these messages. We evaluate
the effectiveness of our approach with a variety of metrics by submitting runs
to the TREC Incident Streams (IS) track: a research initiative specifically
designed for disaster tweet classification and prioritisation. The results
demonstrate that our approach achieves competitive performance in most metrics
as compared to other participating runs. Subsequently, we find that an ensemble
approach combining disparate transformer encoders within our approach helps to
improve the overall effectiveness to a significant extent, achieving
state-of-the-art performance in almost every metric. We make the code publicly
available so that our work can be reproduced and used as a baseline for the
community for future work in this domain.

本研究采用基于 Transformer 的多任务学习方法，对社交媒体上发布的求助信息进行分类和优先级评估，在 TREC INCIDENT STREAMS 赛道上获得了与其他参赛队伍相当的成绩，其中采用集成方法能够进一步提高准确率，本研究将其代码公开，为未来相关研究提供了基准。