Nikesh Gyawali, Iustin Sirbu, Tiberiu Sosea, Sarthak Khanal, Doina Caragea, Traian Rebedea, Cornelia Caragea
| Challenge: | Social media, especially Twitter, has been a melting pot for such debates. |
| Approach: | They propose to annotate tweets relevant to shooting events into three classes: In-Favor, Against, and Neutral. |
| Outcome: | The proposed approach outperforms supervised, semi-supervised, and LLM-based zero-shot models on the dataset. |
Similar Papers
Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for stance detection are topic-specific and cross-target stance. |
| Approach: | They propose a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. |
| Outcome: | The proposed model improves performance on a number of challenging linguistic phenomena. |
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning (2024.lrec-main)
Copied to clipboard
| Challenge: | Stance Reasoner is a model for zero-shot stance detection on social media platforms that can be used to extract opinions from opinionated content. |
| Approach: | They propose a method that leverages explicit reasoning over background knowledge to guide the model’s inference about the document’s stance on a target. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on 3 Twitter datasets, including fully supervised models. |
Adversarial Learning for Zero-Shot Stance Detection on Social Media (2021.naacl-main)
Copied to clipboard
| Challenge: | a new model for zero-shot stance detection on Twitter uses adversarial learning to generalize across topics . previous work on zero- shot stance detector on English social media focuses on cross-target stances . |
| Approach: | They propose a model that uses adversarial learning to generalize across topics on Twitter . their model achieves state-of-the-art performance on unseen test topics . |
| Outcome: | The proposed model achieves state-of-the-art performance on unseen topics with minimal computational costs. |
P-Stance: A Large Dataset for Stance Detection in Political Domain (2021.findings-acl)
Copied to clipboard
| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose to use a large stance detection dataset in the political domain to detect stances on twitter. |
| Outcome: | The proposed model achieves a macro-average F1-score of 80.53% and can be used to improve cross-domain stance detection. |
-Stance: A Large-Scale Real World Dataset of Stances in Legal Argumentation (2025.acl-long)
Copied to clipboard
| Challenge: | Current tools for legal argument reasoning do not support this task. |
| Approach: | They propose to use a large-scale dataset to facilitate work on the legal argument stance classification task by evaluating whether a case summary strengthens or weakens a legal argument. |
| Outcome: | The proposed dataset is used to facilitate work on the legal argument stance classification task, which involves assessing whether a case summary strengthens or weakens a legal argument (polarity) and to what extent (intensity). |
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection (2022.coling-1)
Copied to clipboard
| Challenge: | stance detection is a task to identify attitudes from opinions towards certain targets, but it is expensive and time-consuming . stance detector is based on labeled data, but unlabeled data can be collected easier . |
| Approach: | They propose a semi-supervised framework for few-shot stance detection that uses unlabeled data to learn more distinguishable representations for different targets. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple benchmark datasets. |
Can We Identify Stance without Target Arguments? A Study for Rumour Stance Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing target-aware models underperform in cases where the context of the target is crucial. |
| Approach: | They propose a framework to enhance reasoning with the targets and propose 'target-aware' models without awareness of the target. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark datasets. |
Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for stance detection for pure texts have limited results to multi-modal content. |
| Approach: | They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities. |
| Outcome: | The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter . |
tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing stance detection datasets are complex deep neural networks, making them difficult to interpret. |
| Approach: | They propose a new large dataset free of such biases and demonstrate its aptness on existing stance detection systems. |
| Outcome: | The proposed model achieves human-level performance on the WT–WT dataset and more than two-third accuracy on other datasets. |
Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation (2021.emnlp-main)
Copied to clipboard
| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose a method that applies instance-specific temperature scaling to the teacher and student predictions. |
| Outcome: | The proposed method outperforms the state-of-the-art on all datasets and on multiple datasets. |