Incentive Instruction for Event Relation Extraction in Low-Resource

Abstract

Relation extraction is the foundation for constructing a Event Evolution Graph. However, existing methods for extracting relation are more focus on data sufficient. This paper proposes a method for extracting relation in low-resource domain. Firstly, a incentive instruction is designed for task. We incorporate incentive instruction into the original text, which contain factual descriptions of the relation between defective events and the relation that need to be predicted. Additionally, defective events are included in the incentive instruction, allowing the model to shift its focus from the contextual representation of individual event words to the overall representation of text units. By employing this approach, we transform the relation extraction task into a pre-training task. Specifically, the Bidirectional Encoder Representation from Transformers places relation prediction words in designated positions within the incentive instructions and generates labels for each prediction word. Finally, we obtain the optimal relation. Compared with other models, the proposed method improves the F1 scores of the three logical relations by 14.41% to 23.19%, 15.45% to 28.42%, and 15.33% to 27.02%, respectively.

Keywords

• Incentive instruction
• event relation extraction
• BERT studing