Quantum-Inspired Density Matrix Encoder for Sexual Harassment Personal Stories Classification

Abstract

Nowadays, more and more sexual harassment personal stories have been shared on social media. To better monitor and analyze the extent of sexual harassment based on these social media data, we need to automatically categorize different forms of sexual harassment personal stories. Existing methods apply convolutional neural network (CNN) with different convolution window sizes to this text classification task. However, the previous CNN models do not provide an effective way to synthesize window size-related local representations, but simply concatenate all local representations together. To address this problem, we propose a new density matrix encoder, inspired by quantum mechanics, to encode local representations as particles in quantum state and generate a global representation as quantum mixed system for each story. Experiment on SafeCity dataset shows that our model outperforms CNN baseline and achieves better performance than the state-of-the-art model when considering both accuracy and speed, demonstrating the effectiveness of the proposed density matrix encoder.