mlsanityData to 14 Oct 2026

Exploiting Sentiment and Common Sense for Zero-shot Stance Detection

arXiv 2208.08797 ↗First posted 18 Aug 2022cs.CLcited by 16

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Abstract (arXiv)

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 transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark dataset--VAST. Meanwhile, ablation studies prove the significance of each module in our model. Analysis of the relations between sentiment, common sense, and stance indicates the effectiveness of sentiment and common sense.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 18 Aug 2022.

Builds on

15 of 40 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  3. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  4. 2019Knowledge Enhanced Contextual Word Representationscited by 296
  5. 2020Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representationscited by 98
  6. 2016Stance Detection with Bidirectional Conditional Encodingcited by 93
  7. 2019Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modelingcited by 71
  8. 2016Stance and Sentiment in Tweetscited by 62
  9. 2018Cross-Target Stance Classification with Self-Attention Networkscited by 41
  10. 2021Adversarial Learning for Zero-Shot Stance Detection on Social Mediacited by 37
  11. 2021Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Trainingcited by 28
  12. 2016MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detectioncited by 27
3 more
  1. 2021Semantic Representation for Dialogue Modelingcited by 24
  2. 2020KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysiscited by 16
  3. 2019Improving Relation Extraction with Knowledge-attentioncited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 6; 2025: 3; 2026: 2.

16 papers.

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  1. 2023Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debiascited by 16
  2. 2025Zero-Shot Conversational Stance Detection: Dataset and Approachescited by 11
  3. 2024Can Large Language Models Address Open-Target Stance Detection?cited by 10
  4. 2024Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performancecited by 6
  5. 2023Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactionscited by 5
  6. 2023Ladder-of-Thought: Using Knowledge as Steps to Elevate Stance Detectioncited by 4
  7. 2024Collaborative Knowledge Infusion for Low-resource Stance Detectioncited by 4
  8. 2024Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challengescited by 4
  9. 2024EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detectioncited by 3
  10. 2023Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Informationcited by 1
  11. 2023XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learnerscited by 1
  12. 2024PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarizationcited by 1
4 more
  1. 2025Disentangled VAD Representations via a Variational Framework for Political Stance Detectioncited by 0
  2. 2025Multilingual Target-Stance Extractioncited by 0
  3. 2026MMDS-Bench: Benchmarking Multimodal Large Language Models on Dynamic Stance in Social Media Interactionscited by 0
  4. 2026From Two Passes to One: Compact and Efficient Target-Stance Extractioncited by 0