Twitter Stance Detection via Neural Production Systems

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 knowledg...

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Bibliographic Details
Main Authors: Zhang, Bowen, Ding, Daijun, Xu, Guangning, Guo, Jinjin, Huang, Zhichao, Huang, Xu
Format: Conference Proceeding
Language:English
Subjects:
Blogs
Deep learning
deep neural network
Knowledge engineering
Neural networks
neural production system
Production systems
Signal processing
Social networking (online)
stance detection
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10094597