| Challenge: | EZ-STANCE is a large dataset for zero-shot stance detection in english . it includes both noun-phrase targets and claim targets covering a wide range of domains. |
| Approach: | They present a large English ZSSD dataset with 30,606 annotated text-target pairs . they propose to transform EZ-STANCE into the NLI task by applying two simple yet effective prompts to noun-phrase targets. |
| Outcome: | The proposed dataset includes noun-phrase targets and claim targets covering a wide range of domains. |
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| Challenge: | Recent advances in zero-shot stance detection are limited to English and Chinese . stance can provide useful information for important events such as policymaking and presidential elections. |
| Approach: | They present a Chinese dataset for zero-shot stance detection that is the first for ZSSD. |
| Outcome: | The proposed dataset is the first Chinese dataset for zero-shot stance detection. |
Bilingual Zero-Shot Stance Detection (2025.acl-long)
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| Challenge: | a study focuses on noun-phrase and claim targets within bilingual ZSSD scenarios . a dataset focusing on claim targets with a low occurrence of shared words is also explored . |
| Approach: | They use a bilingual bilingual ZSSD dataset to investigate the use of zero-shot stance detection. |
| Outcome: | The proposed dataset is the first to examine this difficult setting in bilingual ZSSD . it focuses on noun-phrase and claim targets within in-domain and out-of-domain bilingual scenarios . |
Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)
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| Challenge: | Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets. |
| Approach: | They propose a speaker interaction and target-aware prototypical contrastive learning model that can detect public opinion towards specific targets using social media data. |
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Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations (2020.emnlp-main)
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| 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. |
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (2022.acl-long)
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| Challenge: | Existing methods achieve promising performance in in-target stance detection when trained and tested on the same datasets. |
| Approach: | They propose a joint contrastive learning framework to generalize stance features for unseen targets. |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark datasets. |
EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection (2024.lrec-main)
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| Challenge: | Existing methods for enhancing text or data are limited by lack of logical connections between generated texts and training data. |
| Approach: | They propose an encoder-decoder data augmentation framework that combines large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships. |
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ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation (2024.findings-acl)
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| Challenge: | Until recently, zero-shot stance detection was limited to in-domain tasks. |
| Approach: | They propose a method for stance detection that trains a model that can generalize well to unseen targets across multiple domains. |
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Exploiting Sentiment and Common Sense for Zero-shot Stance Detection (2022.coling-1)
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| Challenge: | Existing stance detection models use sentiment and commonsense knowledge to classify stance toward documents and topics . obtaining rich annotated data in stance detector is time-consuming and laborintensive . |
| Approach: | They propose to use sentiment and commonsense knowledge to boost transferability of stance detection model by using sentiment and similar knowledge. |
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Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning (2024.lrec-main)
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| 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. |
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-Stance: A Large-Scale Real World Dataset of Stances in Legal Argumentation (2025.acl-long)
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| 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. |
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