Summary of Stance Reasoner: Zero-shot Stance Detection on Social Media with Explicit Reasoning, by Maksym Taranukhin et al.
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
by Maksym Taranukhin, Vered Shwartz, Evangelos Milios
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach called Stance Reasoner is introduced for zero-shot stance detection on social media platforms, which leverages explicit reasoning over background knowledge to guide the model’s inference about a document’s stance. The method uses a pre-trained language model as a source of world knowledge and combines it with the chain-of-thought in-context learning approach to generate intermediate reasoning steps. This approach outperforms current state-of-the-art models on 3 Twitter datasets, including fully supervised models, while providing explicit and interpretable explanations for its predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Social media is full of opinions! Stance detection helps computers understand people’s views on different topics. A new way to do this called Stance Reasoner uses background knowledge to help the computer make decisions about what someone thinks about a topic. This approach is good at guessing what people think even when it has never seen that topic before, and it can explain its answers in simple terms. |
Keywords
» Artificial intelligence » Inference » Language model » Supervised » Zero shot
