Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations (2010.03640v1)

Abstract: Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.

• The paper introduces a novel dataset (VAST) and the TGA Net model leveraging unsupervised clustering for zero-shot stance detection.
• The model achieves statistically significant macro-average F1 improvements, effectively handling 'pro' stances and complex phenomena like sarcasm.
• The approach enables real-world applications such as misinformation detection and sentiment analysis across varied topics.

Insights into Zero-Shot Stance Detection Using Generalized Topic Representations

The paper, "Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations," introduces novel advancements in the field of computational linguistics, specifically focusing on stance detection—a crucial task that aims to classify an author's position on various topics embedded within text. This work targets the limitations of existing stance detection methods by emphasizing zero-shot learning, which can classify stances without requiring labeled training examples on the new topics.

Overview of Contributions

The primary contributions of the work are twofold: the development of a comprehensive dataset, named VAried Stance Topics (VAST), designed for zero-shot and few-shot stance detection, and introducing a model utilizing generalized topic representations to enhance stance detection capabilities.

The VAST dataset is constructed to address two significant limitations in prior datasets: narrow topic coverage and limited lexical diversity. It encompasses a broad spectrum of themes, such as education, politics, and public health, and captures varied expressions of topics, making it suitable for evaluating models for generalization in zero-shot stance detection.

The researchers further propose an innovative model, TGA Net, which leverages contextual conditional encoding and generalized topic representations obtained via unsupervised clustering. This approach critically aids in understanding topic similarities and relationships, thus enhancing stance detection.

Strong Numerical Results and Bold Claims

The experiments detail TGA Net's capabilities through a comparison with several baselines, including BERT variations and other stance detection models like BiCond and Cross-Net. Notably, TGA Net demonstrates statistically significant improvements in macro-average F1 scores for few-shot stance detection, particularly for the 'pro' stance labels, against traditional BERT-joint models. Furthermore, the method exhibits robust performance in understanding challenging phenomena such as sarcasm and rhetorical complexity, outperforming standard baselines.

Practical and Theoretical Implications

The advancements detailed in this work offer numerous implications:

1. Practical Implications: The zero-shot stance detection model is beneficial in real-world applications where labeled data for thousands of potential topics is impractical to obtain. This has implications for areas such as sentiment analysis, detecting misinformation, and understanding public opinion in domains with rapid topical shifts, like social media platforms.
2. Theoretical Implications: The paper posits a shift towards understanding stance detection through the lens of generalized topic relationships rather than merely sentiment cues. TGA Net's ability to distinguish stance independently from sentiment words suggests potential pathways for disentangling sentiment from stance further in computational models, potentially leading to more nuanced NLP systems.

Future Speculations

In future developments within AI and NLP, this research signals increased attention towards leveraging unsupervised approaches and clustering techniques for topic representations. As zero-shot learning continues to evolve, we could foresee models becoming progressively adept at processing and generalizing across multilingual and multicultural datasets, given the global nature of many discussions. Also, refining methodologies to further detach sentiment dependency could propel stance detection models beyond current limitations into applications involving more politically or ethically charged content.

In summary, this paper constitutes a meaningful advance in stance detection, presenting a robust challenge to traditional methods by proposing novel techniques and data for zero-shot evaluation. The implications echo across various tasks within NLP, advocating for a deeper exploration into generalized topic representations.