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Open Access

Collaborative Knowledge Infusion for Low-Resource Stance Detection

Centre for Frontier AI Research, and Institute of High-Performance Computing, Agency for Science Technology and Research, Singapore 138632, Singapore
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Abstract

Stance detection is the view towards a specific target by a given context (e.g. tweets, commercial reviews). Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly. However, prevailing works for knowledge-infused stance detection predominantly incorporate target knowledge from a singular source that lacks knowledge verification in limited domain knowledge. The low-resource training data further increase the challenge for the data-driven large models in this task. To address those challenges, we propose a collaborative knowledge infusion approach for low-resource stance detection tasks, employing a combination of aligned knowledge enhancement and efficient parameter learning techniques. Specifically, our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment. Additionally, we also introduce the parameter-efficient collaborative adaptor with a staged optimization algorithm, which collaboratively addresses the challenges associated with low-resource stance detection tasks from both network structure and learning perspectives. To assess the effectiveness of our method, we conduct extensive experiments on three public stance detection datasets, including low-resource and cross-target settings. The results demonstrate significant performance improvements compared to the existing stance detection approaches.

Keywords

parameter-efficient learninglow-resource stance detectionknowledge infusion

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 682-698
DOI: 10.26599/BDMA.2024.9020021
Cite this article:
Yan M, Joey TZ, Ivor WT. Collaborative Knowledge Infusion for Low-Resource Stance Detection. Big Data Mining and Analytics, 2024, 7(3): 682-698. https://doi.org/10.26599/BDMA.2024.9020021

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Received: 13 August 2023
Revised: 17 February 2024
Accepted: 22 March 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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