research-article

Knowledge-enhanced Prompt-tuning for Stance Detection

Authors:

Cheng PengAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 6

Article No.: 159, Pages 1 - 20

https://doi.org/10.1145/3588767

Published: 16 June 2023 Publication History

Abstract

Investigating public attitudes on social media is important in opinion mining systems. Stance detection aims to analyze the attitude of an opinionated text (e.g., favor, neutral, or against) toward a given target. Existing methods mainly address this problem from the perspective of fine-tuning. Recently, prompt-tuning has achieved success in natural language processing tasks. However, conducting prompt-tuning methods for stance detection in real-world remains a challenge for several reasons: (1) The text form of stance detection is usually short and informal, which makes it difficult to design label words for the verbalizer. (2) The tweet text may not explicitly give the attitude. Instead, users may use various hashtags or background knowledge to express stance-aware perspectives. In this article, we first propose a prompt-tuning-based framework that performs stance detection in a cloze question manner. Specifically, a knowledge-enhanced prompt-tuning framework (KEprompt) method is designed, which consists of an automatic verbalizer (AutoV) and background knowledge injection (BKI). Specifically, in AutoV, we introduce a semantic graph to build a better mapping from the predicted word of the pretrained language model and detection labels. In BKI, we first propose a topic model for learning hashtag representation and introduce ConceptGraph as the supplement of the target. At last, we present a challenging dataset for stance detection, where all stance categories are expressed in an implicit manner. Extensive experiments on a large real-world dataset demonstrate the superiority of KEprompt over state-of-the-art methods.

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Cited By

Keber MGrubišić IBarešić AJović A(2024)A Review on Neuro-symbolic AI Improvements to Natural Language Processing2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569741(66-72)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569741
Ding DChen RJing LZhang BHuang XDong LZhao XSong G(2024)Cross-Target Stance Detection by Exploiting Target Analytical PerspectivesICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448397(10651-10655)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448397

Index Terms

Knowledge-enhanced Prompt-tuning for Stance Detection
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 6

June 2023

635 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3604597

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 16 June 2023

Online AM: 23 March 2023

Accepted: 13 March 2023

Revised: 08 January 2023

Received: 08 October 2022

Published in TALLIP Volume 22, Issue 6

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Stable Support Project for Shenzhen Higher Education Institutions
Research Promotion Project of Key Construction Discipline in Guangdong Province

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Cited By

Keber MGrubišić IBarešić AJović A(2024)A Review on Neuro-symbolic AI Improvements to Natural Language Processing2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569741(66-72)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569741
Ding DChen RJing LZhang BHuang XDong LZhao XSong G(2024)Cross-Target Stance Detection by Exploiting Target Analytical PerspectivesICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448397(10651-10655)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448397

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