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. |
| Outcome: | The proposed framework significantly improves over state-of-the-art methods on benchmark datasets while enabling interpretable rationale-based learning. |
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Target-Aware Data Augmentation for Stance Detection (2021.naacl-main)
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| Challenge: | Existing methods for stance detection are not diversified or inconsistent with the given target and label information. |
| Approach: | They propose to augment a text with a conditional masked word prediction task . they propose to replace a target mention with 'target-aware' sentences by replacing a reference word with . |
| Outcome: | The proposed method outperforms existing methods on 11 targets. |
LLM-Driven Implicit Target Augmentation and Fine-Grained Contextual Modeling for Zero-Shot and Few-Shot Stance Detection (2025.emnlp-main)
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| Challenge: | Recent studies on zero-shot and few-shot stance detection neglect implicit yet semantically important targets. |
| Approach: | They propose a framework that uses Large Language Models to annotate implicit targets . they also propose 'DyMCA' to dynamically adjust text-target contributions based on context . |
| Outcome: | The proposed framework achieves state-of-the-art on a benchmark dataset. |
EZ-STANCE: A Large Dataset for Zero-Shot Stance Detection (2023.findings-emnlp)
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| 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. |
Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection (2022.emnlp-main)
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| Challenge: | Existing methods for zero-shot stance detection are labor-intensive to train for each new target. |
| Approach: | They propose a generative data augmentation approach to generate training samples containing unseen and seen targets and map them into the same embedding space with contrastive learning. |
| Outcome: | The proposed model achieves state-of-the-art on most topics in the task of zero-shot stance detection. |
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. |
Stanceformer: Target-Aware Transformer for Stance Detection (2024.findings-emnlp)
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| Challenge: | Existing transformer models that lack the capability to prioritize targets under-perform and are underperforming the task. |
| Approach: | They propose a target-aware transformer model that incorporates enhanced attention towards the targets during both training and inference. |
| Outcome: | The proposed model improves on state-of-the-art models and Large Language Models and can be used for other domains. |
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. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark datasets. |
C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection (2023.acl-long)
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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. |
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. |
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. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on 3 Twitter datasets, including fully supervised models. |