Designing in-depth educational questions is a time-consuming and cognitively demanding task. Therefore, it is intriguing to study how to build Question Generation (QG) models to automate the question creation process. However, existing QG datasets are not suitable for educational question generation because the questions are not real questions asked by humans during learning and can be solved by simply searching for information.
To bridge this gap, we present KHANQ, a challenging dataset for educational question generation, containing 1,034 high-quality learner generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. Each data sample is carefully paraphrased and annotated as a triple of 1) Context: an independent paragraph on which the question is based; 2) Prompt: a text prompt for the question (e.g., the learner’s background knowledge); 3) Question: a deep question based on Context and coherent with Prompt. By conducting a human evaluation on the aspects of appropriateness, coverage, coherence, and complexity, we show that state-of-the-art QG models which perform well on shallow question generation datasets have difficulty in generating useful educational questions. This makes KHANQ a challenging testbed for educational question generation for further investigation.

KHANQ: A Dataset for Generating Deep Questions in Education

Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised}multi-hop QA. We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. Using only generated training data, we can train a competent multi-hop QA which achieves 61% and 83% of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data. Our codes are publicly available at https://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA.

Unsupervised Multi-hop Question Answering by Question Generation

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VOS34 - Natural Language Generation, Summarization and Simplification

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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16D-Oral: Question Answering

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KHANQ: A Dataset for Generating Deep Questions in Education

Unsupervised Multi-hop Question Answering by Question Generation

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