Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction

Understand

Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings.

• However, people can grasp new knowledge by learning a few instances.
• To this end, we take the first step to study the few-shot relational triple extraction, which has not been well understood.
• Unlike previous single-task few-shot problems, relational triple extraction is more challenging as the entities and relations have implicit correlations.