A unified framework for contextual and factoid question generation

• Main Authors: DONG, Chenhe, SHEN, Ying, LIN, Shiyang, LIN, Zhenzhou, DENG, Yang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Question generation; multi-task learning; knowledge acquisition; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9085; https://ink.library.smu.edu.sg/context/sis_research/article/10088/viewcontent/10136803.pdf
• Institution: Singapore Management University

Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on the structural knowledge “(Europe, countries_within, United Kingdom)” in FQG. And when meeting the entity “Houston Rockets” in FQG, more background information, such as “an American professional basketball team based in Houston since 1971”, can be found in the related passages of CQG. To this end, we propose a unified framework for the tasks of CQG and FQG, where: (i) two types of task-sharing modules are developed to learn shared contextual and structural knowledge, where the task format is unified with a pseudo passage reformulation strategy; (ii) for the CQG task, a task-specific knowledge module with a knowledge selection and aggregation mechanism is introduced, so as to incorporate more factoid knowledge from external knowledge graphs and alleviate the word ambiguity problem; and (iii) for the FQG task, a task-specific passage module with a multi-level passage fusion mechanism is designed to extract fine-grained word-level knowledge. Experimental results in both automatic and human evaluation show the effectiveness of our proposed method.