Knowledge-Enhanced Question Generation Guided by Interrogative Words

Jinhong Li, Xuejie Zhang, Jin Wang, Xiaobing Zhou

Abstract: Question Generation (QG) focuses on creating relevant questions from a given context, but question-answering texts often contain substantial redundant or irrelevant information. The key to effective QG lies in identifying and selecting relevant phrases. For lengthy contexts, combining these discrete phrases into semantically coherent questions remains a significant challenge. To address the issue of information redundancy in long documents, this paper proposes a method that extracts key information from discrete documents to construct fine-grained text representations. This method incorporates inductive biases to support the Question Generation process. Additionally, the paper introduces an interrogative word type predictor that accurately identifies interrogative words and determines the domain of the answer, guiding the model in generating semantically aligned questions. This ensures that the generated questions are closely aligned with the answers and their respective context. The proposed method significantly reduces information redundancy and the reliance on annotated data. Experimental results on two benchmark data sets demonstrate that the proposed model achieves performance comparable to that of state-of-the-art QG methods.

External IDs:dblp:conf/ijcnn/LiZWZ25