论文标题
实时灾难事件的与事件有关的偏差删除
Event-Related Bias Removal for Real-time Disaster Events
论文作者
论文摘要
社交媒体已成为分享有关危机事件（例如自然灾害和大规模攻击）信息的重要工具。检测包含有用信息的可行帖子需要实时快速分析大量数据。由于大量帖子不包含任何可操作的信息，因此提出了一个复杂的问题。此外，实时系统中信息的分类需要对室外数据进行培训，因为我们没有新的新兴危机中的任何数据。先前的工作着重于对类似事件类型进行预训练的模型。但是，这些模型捕获了不必要的特定事件偏见，例如事件的位置，这会影响分类器在新兴新事件中新的看不见数据上的分类器的普遍性和性能。在我们的工作中，我们训练一个对抗性神经模型，以消除特定于特定事件的偏见并提高推文重要性分类的性能。
Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. This poses a complex problem due to the large amount of posts that do not contain any actionable information. Furthermore, the classification of information in real-time systems requires training on out-of-domain data, as we do not have any data from a new emerging crisis. Prior work focuses on models pre-trained on similar event types. However, those models capture unnecessary event-specific biases, like the location of the event, which affect the generalizability and performance of the classifiers on new unseen data from an emerging new event. In our work, we train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
