Infusing Knowledge from Wikipedia to Enhance Stance Detection

He, Zihao; Mokhberian, Negar; Lerman, Kristina

doi:10.48550/arxiv.2204.03839

Cited by 2 publications

(4 citation statements)

References 23 publications

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“…The incorporation of background knowledge to enhance stance detection capabilities has garnered significant interest, representing a promising strategy to amplify its efficacy [29]. He et al [13] introduced an approach that integrates target-related background knowledge, such as encyclopedic information from Wikipedia, and devised a fine-tuning methodology to augment the model's learning proficiency. In a similar vein, Liu et al [10] constructed a knowledge graph representing background knowledge and employed graph neural network techniques to develop an advanced stance prediction model.…”

Section: Background Knowledge Enhanced Stance Detection Methodsmentioning

confidence: 99%

“…Recently, some research has addressed the issue of data sparsity by integrating external knowledge, thus enhancing both the performance and the interpretability of stance detection processes. For example, He et al [13] augmented text classifiers by supplementing them with relevant Wikipedia documents about the target. Diaz et al [14] constructed a stance tree using external knowledge extracted from a knowledge base and utilized it as evidence to enhance stance prediction and detection precision.…”

Section: Introductionmentioning

confidence: 99%

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Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

Ding,

Dai,

Peng

et al. 2024

Mathematics

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Investigating public attitudes on social media is crucial for opinion mining systems. Stance detection aims to predict the attitude towards a specific target expressed in a text. However, effective neural stance detectors require substantial training data, which are challenging to curate due to the dynamic nature of social media. Moreover, deep neural networks (DNNs) lack explainability, rendering them unsuitable for scenarios requiring explanations. We propose a distantly supervised explainable stance detection framework (DS-ESD), comprising an instruction-based chain-of-thought (CoT) method, a generative network, and a transformer-based stance predictor. The CoT method employs prompt templates to extract stance detection explanations from a very large language model (VLLM). The generative network learns the input-explanation mapping, and a transformer-based stance classifier is trained with VLLM-annotated stance labels, implementing distant supervision. We propose a label rectification strategy to mitigate the impact of erroneous labels. Experiments on three benchmark datasets showed that our model outperformed the compared methods, validating its efficacy in stance detection tasks. This research contributes to the advancement of explainable stance detection frameworks, leveraging distant supervision and label rectification strategies to enhance performance and interpretability.

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Section: Background Knowledge Enhanced Stance Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

Ding,

Dai,

Peng

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…The use of background knowledge to enhance the performance of stance detection has garnered attention as an effective approach to improving performance [28]. For instance, He et al [11] introduced target-related background knowledge, such as Wikipedia knowledge, and proposed a fine-tuning learning method to improve the model's learning ability. Similarly, Luo et al [10] constructed background knowledge as a knowledge graph and utilized graph neural network methods to develop a stance predictor.…”

Section: Background Knowledge Enhanced Stancementioning

confidence: 99%

“…Recently, some pioneering studies have been conducted to address the sparsity problem by utilizing external knowledge to enhance the performance and interpretability of stance detection. For example, He et al [11] improved the performance of text classifiers by introducing target-related Wikipedia documents as content supplements. Diaz et al [12] constructed a stance tree by retrieving external knowledge from a knowledge base and used it as evidence to support stance prediction, thus enhancing the accuracy of stance detection.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Chain-of-Thought to Enhance Stance Detection with Prompt-Tuning

Ding,

Fu,

Peng

et al. 2024

Mathematics

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Investigating public attitudes towards social media is crucial for opinion mining systems to gain valuable insights. Stance detection, which aims to discern the attitude expressed in an opinionated text towards a specific target, is a fundamental task in opinion mining. Conventional approaches mainly focus on sentence-level classification techniques. Recent research has shown that the integration of background knowledge can significantly improve stance detection performance. Despite the significant improvement achieved by knowledge-enhanced methods, applying these techniques in real-world scenarios remains challenging for several reasons. Firstly, existing methods often require the use of complex attention mechanisms to filter out noise and extract relevant background knowledge, which involves significant annotation efforts. Secondly, knowledge fusion mechanisms typically rely on fine-tuning, which can introduce a gap between the pre-training phase of pre-trained language models (PLMs) and the downstream stance detection tasks, leading to the poor prediction accuracy of the PLMs. To address these limitations, we propose a novel prompt-based stance detection method that leverages the knowledge acquired using the chain-of-thought method, which we refer to as PSDCOT. The proposed approach consists of two stages. The first stage is knowledge extraction, where instruction questions are constructed to elicit background knowledge from a VLPLM. The second stage is the multi-prompt learning network (M-PLN) for knowledge fusion, which learns model performance based on the background knowledge and the prompt learning framework. We evaluated the performance of PSDCOT on publicly available benchmark datasets to assess its effectiveness in improving stance detection performance. The results demonstrate that the proposed method achieves state-of-the-art results in in-domain, cross-target, and zero-shot learning settings.

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Infusing Knowledge from Wikipedia to Enhance Stance Detection

Cited by 2 publications

References 23 publications

Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

Leveraging Chain-of-Thought to Enhance Stance Detection with Prompt-Tuning

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