Twitter Stance Detection via Neural Production Systems
Bowen Zhang, Daijun Ding, Guangning Xu, Jinjin Guo, Zhichao Huang, Xu Huang
Abstract
Stance detection is an important task, which aims to classify the attitude of an opinionated text toward a given target. In this paper, we develop an interpretable neural production system for stance detection (NPS4SD). NPS4SD is an end-to-end deep learning model, which consists of a set of knowledge rules that are applied by binding with specific entities. NPS4SD consists of two main components: a pretrained model for learning the text representation and a variable binding network (VBN) to bind the knowledge rules with text entities. Extensive experiments are conducted to evaluate the effectiveness of the proposed NPS4SD model on three real-world datasets with in-domain, cross-target and zero-shot setups. Experimental results demonstrate that NPS4SD achieves substantially better performance than the strong competitors for the stance detection task.
BibTeX
@inproceedings{icassp2023_twitterstancedet,
title = {Twitter Stance Detection via Neural Production Systems},
author = {Bowen Zhang and Daijun Ding and Guangning Xu and Jinjin Guo and Zhichao Huang and Xu Huang},
booktitle = {ICASSP 2023},
year = {2023}
}