Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges

Abstract: Stance detection is the task of determining the viewpoint expressed in a text towards a given target. A specific direction within the task focuses on cross-target stance detection, where a model trained on samples pertaining to certain targets is then applied to a new, unseen target. With the increasing need to analyze and mining viewpoints and opinions online, the task has recently seen a significant surge in interest. This review paper examines the advancements in cross-target stance detection over the last decade, highlighting the evolution from basic statistical methods to contemporary neural and LLM-based models. These advancements have led to notable improvements in accuracy and adaptability. Innovative approaches include the use of topic-grouped attention and adversarial learning for zero-shot detection, as well as fine-tuning techniques that enhance model robustness. Additionally, prompt-tuning methods and the integration of external knowledge have further refined model performance. A comprehensive overview of the datasets used for evaluating these models is also provided, offering valuable insights into the progress and challenges in the field. We conclude by highlighting emerging directions of research and by suggesting avenues for future work in the task.

• The paper presents a comprehensive survey of cross-target stance detection methods, spanning from early statistical approaches to advanced LLM-based techniques.
• It details methodologies like fine-tuning and prompt-tuning, with a focus on knowledge-enhanced models to improve zero-shot performance.
• It highlights key challenges such as generalizing to unseen targets and integrating external knowledge for enhanced interpretability and trust.

Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges

Introduction

Cross-target stance detection (CTSD) has emerged as a pivotal area of research in NLP, driven by the rapid growth in social media and the need to analyze vast amounts of opinionated content. Unlike traditional stance detection, which is confined to predefined targets, cross-target stance detection involves determining the stance toward targets not seen during the training phase, rendering it highly challenging.

Evolution and Models in Cross-Target Stance Detection

Over the past several years, the field has witnessed an evolution from basic statistical methods to sophisticated neural network architectures and, more recently, the integration of LLMs and knowledge-enhanced methodologies. Here, we categorize the various approaches into five major types: Statistics-based methods, Fine-tuning-based methods, Prompt-tuning-based methods, Knowledge-enhanced methods, and Knowledge-enhanced Prompt-tuning methods.

Statistics-based Methods

The early methods in CTSD leaned heavily on statistical techniques to handle stance detection tasks. Notable examples include:

• BiCond: Utilizes bidirectional conditional LSTM encoding, demonstrating a notable improvement in stance detection on unseen targets.
• CrossNet: Implements a self-attention mechanism to capture domain-specific information from a source target, improving generalization to a destination target.
• VTN (Variational Topic Network): Employs neural variational inference and adversarial training to extract and transfer shared topic knowledge between targets.
• TOAD: Utilizes adversarial learning to generate topic-invariant representations, enhancing zero-shot stance detection capabilities.

Fine-tuning Based Methods

Fine-tuning on domain-specific datasets has proved effective. Noteworthy models include:

• BertEmb: Combines BERT embeddings with multi-layer perceptron (MLP) for stance detection and evidence retrieval.
• DTCL: Utilizes supervised contrastive learning to connect target-specific stance information in a latent space.
• GDA-CL: Integrates adversarial networks and hybrid contrastive learning to generate quality synthetic data for unseen targets.
• STANCE-C3: Combines domain counterfactual generation with contrastive learning to improve generalization across targets.

Prompt-tuning Based Methods

Prompt-tuning leverages task-specific prompts or templates to guide LLMs in stance detection:

• PET (Pattern-Exploiting Training): Uses cloze-style prompts to assign soft labels, improving performance with limited labeled data.
• TAPD (Target-Aware Prompt Distillation): Adapts prompts to be target-aware, leveraging multi-prompt distillation.
• MTFF (Multi-perspective Transferable Feature Fusion): Combines target-keyword masking and instance-wise contrastive learning for zero-shot stance detection.
• Stance Reasoner: Utilizes the chain-of-thought (CoT) approach with LLMs for enhanced contextual understanding in stance predictions.

Knowledge-enhanced Methods

Incorporating external domain-specific or commonsense knowledge enhances model performance:

• SEKT (Semantic-Emotion Knowledge Transferring): Constructs a semantic-emotion heterogeneous graph to capture multi-hop semantic connections.
• WS-BERT: Infuses Wikipedia knowledge into stance encoding, improving stance detection on targets with scarce data.
• ANEK (Adversarial Network with External Knowledge): Integrates sentiment information and common sense knowledge with adversarial learning for robust zero-shot stance detection.

Knowledge-enhanced Prompt-tuning Based Methods

Combining knowledge-enhanced approaches with prompt-tuning further refines stance detection:

• KEprompt: Uses automatic verbalizers and integrates external knowledge from sources like SenticNet for robust stance detection.
• INJECT: Employs a dual-encoder architecture with cross-attention mechanisms and external knowledge for context-enhanced stance detection.
• LKI-BART: Leverages LLM-driven contextual knowledge and a prototypical contrastive loss to align stance representations with semantic labels.

Datasets in Cross-Target Stance Detection

The development and evaluation of CTSD models are supported by various datasets tailored for stance detection:

• SemEval-2016 Task 6: Includes targets like Donald Trump and the Feminist Movement.
• RumourEval (2017, 2019): Focuses on rumor classification alongside stance detection.
• VAST: A diverse dataset covering topics such as politics, health, and education, suited for zero-shot scenarios.
• COVID-19 Stance: Captures public sentiment towards pandemic-related topics.

Open Challenges and Future Directions

Despite significant advancements, several challenges persist:

1. Generalization to Unseen Targets: Current models often fall short when faced with completely new targets.
2. Effective Knowledge Integration: Managing and dynamically applying diverse knowledge sources remains a complex task.
3. Interpretability and Transparency: Explaining model decisions in complex cross-target scenarios is essential for trust and understanding.

Clarifying these aspects demands novel strategies, such as real-time knowledge retrieval systems, explainable AI techniques, and dynamic adaptation mechanisms. The increasing use of LLMs in NLP offers promising avenues for robust cross-target stance detection, demanding comprehensive exploration.

Conclusion

Cross-target stance detection remains a dynamic and highly relevant field within NLP. Continuous advancements in methodology, encompassing statistics, fine-tuning, and knowledge-enhanced prompt-tuning, have significantly elevated model performance. However, addressing prevailing challenges related to generalization, knowledge integration, and interpretability will be crucial for future breakthroughs, enhancing the capabilities of stance detection models across varied and evolving targets.