Papers Zero-Shot Stance Detection
“Zero-Shot Stance Detection” 태그가 달린 논문 18편 · 필터 해제
Abstract, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning
Zero-shot stance detection (ZSSD) aims to identify the stance of text toward previously unseen targets, a setting where conventional supervised models often fail due to reliance on labeled data and shallow lexical cues. …
Stance DetectionZero-Shot Stance DetectionTracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites
Understanding how misleading and outright false information enters news ecosystems remains a difficult challenge that requires tracking how narratives spread across thousands of fringe and mainstream news websites. To do…
Fact CheckingStance DetectionZero-Shot Stance DetectionA More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs
Group polarization is an important research direction in social media content analysis, attracting many researchers to explore this field. Therefore, how to effectively measure group polarization has become a critical to…
Stance DetectionZero-Shot Stance DetectionZero-Shot Stance Detection using Contextual Data Generation with LLMs
Stance detection, the classification of attitudes expressed in a text towards a specific topic, is vital for applications like fake news detection and opinion mining. However, the scarcity of labeled data remains a chall…
Fake News DetectionFew-Shot LearningOpinion MiningStance Detection+1EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection
Stance detection aims to determine the attitude expressed in text towards a given target. Zero-shot stance detection (ZSSD) has emerged to classify stances towards unseen targets during inference. Recent data augmentatio…
Data AugmentationDecoderDiversityStance Detection+2Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
Social media platforms are rich sources of opinionated content. Stance detection allows the automatic extraction of users' opinions on various topics from such content. We focus on zero-shot stance detection, where the m…
Few-Shot Stance DetectionIn-Context LearningLanguage ModelingLanguage Modelling+3Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
In this paper, we develop an LLM-powered framework for the curation and evaluation of emerging opinion mining in online health communities. We formulate emerging opinion mining as a pairwise stance detection problem betw…
Opinion MiningStance DetectionZero-Shot Stance DetectionBenchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance
We investigate the performance of LLM-based zero-shot stance detection on tweets. Using FlanT5-XXL, an instruction-tuned open-source LLM, with the SemEval 2016 Tasks 6A, 6B, and P-Stance datasets, we study the performanc…
BenchmarkingStance DetectionZero-Shot Stance DetectionMitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration
Stance detection is critical for understanding the underlying position or attitude expressed toward a topic. Large language models (LLMs) have demonstrated significant advancements across various natural language process…
counterfactualDomain GeneralizationStance DetectionZero-Shot Stance DetectionA Logically Consistent Chain-of-Thought Approach for Stance Detection
Zero-shot stance detection (ZSSD) aims to detect stances toward unseen targets. Incorporating background knowledge to enhance transferability between seen and unseen targets constitutes the primary approach of ZSSD. Howe…
Stance DetectionZero-Shot Stance DetectionStance Detection: A Practical Guide to Classifying Political Beliefs in Text
Stance detection is identifying expressed beliefs in a document. While researchers widely use sentiment analysis for this, recent research demonstrates that sentiment and stance are distinct. This paper advances text ana…
In-Context LearningNatural Language InferenceSentiment AnalysisStance Detection+2OpenStance: Real-world Zero-shot Stance Detection
Prior studies of zero-shot stance detection identify the attitude of texts towards unseen topics occurring in the same document corpus. Such task formulation has three limitations: (i) Single domain/dataset. A system is …
Domain GeneralizationNatural Language InferenceStance DetectionZero-Shot Stance DetectionZero-shot stance detection based on cross-domain feature enhancement by contrastive learning
Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-invariant features is critical for zero-sho…
Contrastive LearningStance DetectionZero-Shot Stance Detection基于主题提示学习的零样本立场检测方法(A Topic-based Prompt Learning Method for Zero-Shot Stance Detection)
“零样本立场检测目的是针对未知目标数据进行立场极性预测。一般而言,文本的立场表达是与所讨论的目标主题是紧密联系的。针对未知目标的立场检测,本文将立场表达划分为两种类型:一类在说话者面向不同的主题和讨论目标时表达相同的立场态度,称之为目标无关的表达;另一类在说话者面向特定主题和讨论目标时才表达相应的立场态度,本文称之为目标依赖的表达。对这两种表达进行区分,有效学习到目标无关的表达方式并忽略目标依赖的表达方式,有望强化模型的可迁移能力,使其…
Prompt LearningStance DetectionZero-Shot Stance DetectionExploiting Sentiment and Common Sense for Zero-shot Stance Detection
The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transfera…
Common Sense ReasoningStance DetectionZero-Shot Stance DetectionJointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection
Zero-shot stance detection (ZSSD) aims to detect the stance for an unseen target during the inference stage. In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastiv…
Contrastive LearningStance DetectionZero-Shot Stance DetectionAdversarial Learning for Zero-Shot Stance Detection on Social Media
Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learn…
Stance DetectionZero-Shot Stance DetectionZero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
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-sh…
Stance DetectionZero-Shot Stance Detection