Twitter Stance Detection via Neural Production Systems

Bowen Zhang; Daijun Ding; Guangning Xu; Jinjin Guo; Zhichao Huang; Xu Huang

doi:10.1109/ICASSP49357.2023.10094597

Twitter Stance Detection via Neural Production Systems

Bowen Zhang^*
, Daijun Ding
, Guangning Xu^*
, Jinjin Guo
, Zhichao Huang
, Xu Huang

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

9 Citations (Scopus)

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.

Original language	English
Title of host publication	ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9781728163277
ISBN (Print)	9781728163284
DOIs	https://doi.org/10.1109/ICASSP49357.2023.10094597
Publication status	Published - 4 Jun 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing - Rodos Palace Luxury Convention Resort, Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023 https://ieeexplore.ieee.org/xpl/conhome/10094559/proceeding (Conference proceeding) https://2023.ieeeicassp.org/ (Conference website) https://2023.ieeeicassp.org/program-at-a-glance/ (Conference program)

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2023-June
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated title	ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	4/06/23 → 10/06/23
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10094559/proceeding (Conference proceeding) https://2023.ieeeicassp.org/ (Conference website) https://2023.ieeeicassp.org/program-at-a-glance/ (Conference program)

User-Defined Keywords

deep neural network
neural production system
stance detection

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICASSP49357.2023.10094597

Cite this

Zhang, B., Ding, D., Xu, G., Guo, J., Huang, Z., & Huang, X. (2023). Twitter Stance Detection via Neural Production Systems. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June). IEEE. https://doi.org/10.1109/ICASSP49357.2023.10094597

@inproceedings{f6c5f0c2fe884d6f80164e867c2db16d,

title = "Twitter Stance Detection via Neural Production Systems",

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.",

keywords = "deep neural network, neural production system, stance detection",

author = "Bowen Zhang and Daijun Ding and Guangning Xu and Jinjin Guo and Zhichao Huang and Xu Huang",

note = "This research is supported by the Stable Support Project for Shenzhen Higher Education Institutions (SZWD2021011) and the Research Promotion Project of Key Construction Discipline in Guangdong Province (2022ZDJS112). Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",

year = "2023",

month = jun,

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doi = "10.1109/ICASSP49357.2023.10094597",

language = "English",

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series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/10094559/proceeding, https://2023.ieeeicassp.org/, https://2023.ieeeicassp.org/program-at-a-glance/",

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Zhang, B, Ding, D, Xu, G, Guo, J, Huang, Z & Huang, X 2023, Twitter Stance Detection via Neural Production Systems. in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, IEEE, 48th IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes Island, Greece, 4/06/23. https://doi.org/10.1109/ICASSP49357.2023.10094597

Twitter Stance Detection via Neural Production Systems. / Zhang, Bowen; Ding, Daijun; Xu, Guangning et al.
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. IEEE, 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Twitter Stance Detection via Neural Production Systems

AU - Zhang, Bowen

AU - Ding, Daijun

AU - Xu, Guangning

AU - Guo, Jinjin

AU - Huang, Zhichao

AU - Huang, Xu

N1 - This research is supported by the Stable Support Project for Shenzhen Higher Education Institutions (SZWD2021011) and the Research Promotion Project of Key Construction Discipline in Guangdong Province (2022ZDJS112). Publisher Copyright: © 2023 IEEE.

PY - 2023/6/4

Y1 - 2023/6/4

N2 - 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.

AB - 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.

KW - deep neural network

KW - neural production system

KW - stance detection

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DO - 10.1109/ICASSP49357.2023.10094597

M3 - Conference proceeding

AN - SCOPUS:85177558370

SN - 9781728163284

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings

PB - IEEE

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing

Y2 - 4 June 2023 through 10 June 2023

ER -

Twitter Stance Detection via Neural Production Systems

Abstract

Publication series

Conference

User-Defined Keywords

UN SDGs

Access to Document

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