Zero-shot stance detection based on multi-expert collaboration.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Zero-shot stance detection is crucial for understanding user opinions on emerging topics.
• Effective transfer learning from known to unseen topics remains a challenge.

Purpose of the Study:

• To develop a robust zero-shot stance detection framework.
• To improve feature alignment and transferability for novel topics.

Main Methods:

• A multi-expert cooperative learning framework was designed.
• Key components include multi-expert feature extraction and a gating mechanism for feature selection.
• A specialized learning strategy decomposes complex semantic features.

Main Results:

• The proposed model significantly outperforms existing baseline models on standard benchmark datasets.
• The multi-expert approach enhances the transferability of textual features.
• The gating mechanism effectively filters and fuses features for optimized stance classification.

Conclusions:

• The developed framework offers a superior solution for zero-shot stance detection.
• Multi-expert cooperative learning and selective feature fusion are effective strategies for this task.