Request Correction

Prompt Tuning for Few-shot Relation Extraction via Modeling Global and Local Graphs

Recently, prompt-tuning has achieved very significant results for few-shot tasks. The core idea of prompt-tuning is to insert prompt templates into the input, thus converting the classification task into a masked language modeling problem. However, for few-shot relation extraction tasks, how to mine more information from limited resources becomes particularly important. In this paper, we first construct a global relation graph based on label consistency to optimize the feature representation of samples between different relations. Then the global relation graph is further divided to form a local relation subgraph for each relation type to optimize the feature representation of samples within the same relation. This fully uses the limited supervised information and improves the tuning efficiency. In addition, the existence of rich semantic knowledge in relation labels cannot be ignored. For this reason, this paper incorporates the knowledge in relation labels into prompt-tuning. Specifically, the potential knowledge implicit in relation labels is injected into constructing learnable prompt templates. In this paper, we conduct extensive experiments on four datasets under low-resource settings, showing that this method achieves significant results.