Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs

TL;DR

This paper introduces a deep learning approach using hierarchical LSTMs and supervised attention mechanisms to improve police killing detection in text, achieving state-of-the-art results.

Contribution

It presents a novel deep learning model with supervised attention for police killing detection, surpassing previous EM-based methods that relied on hand-crafted features.

Findings

Achieves state-of-the-art performance in police killing detection

Demonstrates the effectiveness of supervised attention mechanisms

Shows benefits of hierarchical LSTMs in modeling multi-sentence contexts

Abstract

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.