Summary of Cosd: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning, by Yinghan Cheng et al.
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning
by Yinghan Cheng, Qi Zhang, Chongyang Shi, Liang Xiao, Shufeng Hao, Liang Hu
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called CoSD (Collaborative Stance Detection) to improve stance detection, which identifies viewpoints on controversial topics. Current neural models for stance detection suffer from limitations such as lack of explainability and sensitivity to data structure. CoSD leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels. The framework consists of latent Dirichlet allocation (LDA) for implicit topic modeling, contrastive graph learning, and a Collaboration Propagation Aggregation (CPA) module. During inference, CoSD uses a hybrid similarity scoring module to incorporate topic-aware semantics and collaborative signals. Experimental results on two benchmark datasets demonstrate the state-of-the-art performance of CoSD, verifying its effectiveness and explainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about improving how computers can understand people’s opinions on certain topics. Right now, computer models for this task have some big limitations. The authors propose a new way to do it called CoSD (Collaborative Stance Detection). This method combines different types of information from texts and topics to make better predictions. It uses a special kind of math to organize all the information in a way that helps computers understand people’s opinions. The results show that this method is really good at identifying people’s viewpoints, making it a useful tool for understanding how people think about important issues. |
Keywords
» Artificial intelligence » Inference » Semantics
