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Identifying Infrastructure Damage during Earthquake using Deep Active Learning

机译：使用深度主动学习识别地震期间的基础设施破坏

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Twitter provides important information for emergency responders in the rescue process during disasters. However, tweets containing relevant information are sparse and are usually hidden in a vast set of noisy contents. This leads to inherent challenges in generating suitable training data that are required for neural network models. In this paper, we study the problem of retrieving the infrastructure damage information from tweets generated from different location during crisis using the model actively trained on past but similar events. We combine RNN and GRU based model coupled with active learning that gets trained on most uncertain samples and captures the latent features of different data distribution. It reduces the uses of around 90% less training data, thereby significantly reducing the manual annotation efforts. We use the model pre-trained using active learning based approach to retrieve the infrastructure damage tweets originated from different regions. We obtain a minimum of 18% gain on F1-measure and considerably on other metrics over recent state-of-the-art IR techniques.
机译：Twitter在灾难期间为应急人员提供了重要的信息，以帮助他们进行救援。但是，包含相关信息的推文很少，通常隐藏在大量嘈杂的内容中。这在生成神经网络模型所需的合适训练数据方面带来了固有的挑战。在本文中，我们研究使用在过去但类似事件中积极训练的模型，从危机期间从不同位置生成的推文中检索基础设施损坏信息的问题。我们将基于RNN和GRU的模型与主动学习相结合，可以对大多数不确定的样本进行训练，并捕获不同数据分布的潜在特征。它减少了约90％的训练数据使用量，从而大大减少了手动注释工作。我们使用基于主动学习的方法进行预训练的模型，以检索源自不同地区的基础设施破坏鸣叫。与最近的最新IR技术相比，我们在F1测量上获得了至少18％的收益，在其他指标上获得了可观的收益。

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