KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
This work addresses the problem of reducing manual effort and enhancing accuracy in relation extraction for NLP practitioners, representing an incremental improvement over existing prompt-tuning methods.
The paper tackled the challenge of designing effective prompt templates for relation extraction by incorporating semantic and prior knowledge among relation labels into prompt-tuning, resulting in improved performance across five datasets in both standard and low-resource settings.
Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language modeling problem. However, for relation extraction, determining an appropriate prompt template requires domain expertise, and it is cumbersome and time-consuming to obtain a suitable label word. Furthermore, there exists abundant semantic and prior knowledge among the relation labels that cannot be ignored. To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt). Specifically, we inject latent knowledge contained in relation labels into prompt construction with learnable virtual type words and answer words. Then, we synergistically optimize their representation with structured constraints. Extensive experimental results on five datasets with standard and low-resource settings demonstrate the effectiveness of our approach. Our code and datasets are available in https://github.com/zjunlp/KnowPrompt for reproducibility.