谁被警察杀死：为分层LSTM引入监督注意力
Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs
摘要：
随着警察枪击事件越来越受到公众的关注，寻找被警察杀害的人的名字变得越来越重要（警察杀人检测）。不幸的是，在文献中没有太多的工作来解决这个问题。该领域的早期工作\cite{keith2017identifying}提出了一种基于期望最大化（EM）的远程监督框架来处理文档中名称的多次出现。然而，这种基于EM的框架不能充分利用深度学习模型，需要使用手工设计的特征来提高检测性能。在这项工作中，我们提出了一种新的深度学习方法来解决警察杀人识别问题。该方法采用分层LSTM对包含感兴趣人名的多个句子进行建模，并引入基于语义词表和依赖树的监督注意力机制，对重要上下文词语进行加权.我们的实验证明了所提出的模型的好处，并产生最先进的性能，警察杀人检测。
英文摘要：
Finding names of people killed by police has become increasingly important as police shootings get more and more public attention (police killing detection). Unfortunately, there has been not much work in the literature addressing this problem. The early work in this field \cite{keith2017identifying} proposed a distant supervision framework based on Expectation Maximization (EM) to deal with the multiple appearances of the names in documents. However, such EM-based framework cannot take full advantages of deep learning models, necessitating the use of hand-designed features to improve the detection performance. In this work, we present a novel deep learning method to solve the problem of police killing recognition. The proposed method relies on hierarchical LSTMs to model the multiple sentences that contain the person names of interests, and introduce supervised attention mechanisms based on semantical word lists and dependency trees to upweight the important contextual words. Our experiments demonstrate the benefits of the proposed model and yield the state-of-the-art performance for police killing detection.
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