Few-Shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings

Abstract

Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification scenarios, where even in few-shot training settings the model needs to predict the stance towards new targets for which the model has only seen few relevant samples during training. To address the cross-target stance detection in social media by leveraging the social nature of the task, we introduce CT-TN, a novel model that aggregates multimodal embeddings derived from both textual and network features of the data. We conduct experiments in a few-shot cross-target scenario on six different combinations of source-destination target pairs. By comparing CT-TN with state-of-the-art cross-target stance detection models, we demonstrate the effectiveness of our model by achieving average performance improvements ranging from 11% to 21% across different baseline models. Experiments with different numbers of shots show that CT-TN can outperform other models after seeing 300 instances of the destination target. Further, ablation experiments demonstrate the positive contribution of each of the components of CT-TN towards the final performance. We further analyse the network interactions between social media users, which reveal the potential of using social features for crosstarget stance detection.

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

“…Hardalov et al [15] proposed a novel semi-supervised approach to address the issue of scarce data in cross-language scenarios. Khiabaniet al [16] enhanced stance detection performance in low-shot cross-target scenarios through multimodal embeddings derived from both textual and network features of the data. Although these works have achieved improvements in the performance and interpretability, these methods still face the following challenges in practical applications: (1) Most existing methods require the design of complex attention mechanisms to filter out noise and extract task-related background knowledge.…”

VL-Few: Vision Language Alignment for Multimodal Few-Shot Meta Learning

“…This problem is more serious in the few-shot scenario because of scarce training data. So, to alleviate the low-resource problem and improve the generalization ability of the model, meta learning [33][34][35][36][37][38] and few-shot learning [39][40][41][42][43][44][45][46][47][48][49][50][51][52] can be considered. Meta learning can construct a task pool to improve the generalization ability of the model.…”