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Found 7 papers, 2 papers with code
Automatically Generating Cause-and-Effect Questions from Passages
Automated question generation has the potential to greatly aid in education applications, such as online study aids to check understanding of readings.
Educational Multi-Question Generation for Reading Comprehension
Automated question generation has made great advances with the help of large NLP generation models.
Pragmatically Appropriate Diversity for Dialogue Evaluation
To remedy this, we propose the notion of Pragmatically Appropriate Diversity, defined as the extent to which a conversation creates and constrains the creation of multiple diverse responses.
Semantic Diversity in Dialogue with Natural Language Inference
Second, we demonstrate how to iteratively improve the semantic diversity of a sampled set of responses via a new generation procedure called Diversity Threshold Generation, which results in an average 137% increase in NLI Diversity compared to standard generation procedures.
More Diverse Dialogue Datasets via Diversity-Informed Data Collection
Automated generation of conversational dialogue using modern neural architectures has made notable advances.
CIMA: A Large Open Access Dialogue Dataset for Tutoring
To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns.
Multiple Choice Question Generation Utilizing An Ontology
An in-depth analysis of the teachers{'} comments yields useful insights for any researcher working on automated question generation for educational applications.
