Aggregating pairwise semantic differences for few-shot claim verification.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Information Retrieval

Background:

• Automated fact-checking requires verifying claims against evidence.
• Limited labeled datasets hinder claim verification, especially in new domains.
• Existing methods struggle with few-shot learning scenarios.

Purpose of the Study:

• Introduce Semantic Embedding Element-wise Difference (SEED), a novel vector-based method for few-shot claim verification.
• Address the scarcity of labeled data in claim verification tasks.
• Improve the performance of automated fact-checking systems.

Main Methods:

• Developed a vector-based approach aggregating pairwise semantic differences for claim-evidence pairs.
• Proposed simulating class representative vectors capturing average semantic differences.
• Compared SEED against fine-tuned BERT/RoBERTa models and a language model perplexity method.

Main Results:

• SEED demonstrated consistent improvements over competitive baselines in few-shot settings.
• Achieved superior performance on the FEVER and SCIFACT datasets.
• The proposed method effectively handles limited labeled data.

Conclusions:

• SEED offers a robust solution for few-shot claim verification.
• The method shows promise for enhancing automated fact-checking pipelines.
• Availability of code facilitates further research and application.