| Challenge: | Existing QG datasets are not suitable for educational question generation because the questions are not real questions asked by humans during learning. |
| Approach: | They propose a dataset for question generation that contains 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. |
| Outcome: | The proposed dataset contains 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. |
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| Challenge: | Recent question generation approaches use the sequence-to-sequence framework to optimize the log likelihood of ground-truth questions using teacher forcing. |
| Approach: | They propose to optimize for QG-specific objectives via reinforcement learning to improve question quality. |
| Outcome: | The proposed model improves the fluency, relevance, and answerability of generated questions. |
Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference (2023.findings-acl)
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| Challenge: | Existing methods for commonsense question generation produce shallow questions that can be answered by simple word matching. |
| Approach: | They propose a task of commonsense question generation that aims to yield deep-level questions from the text. |
| Outcome: | The proposed model can yield deep-level and to-the-point questions from the text. |
Socratic Question Generation: A Novel Dataset, Models, and Evaluation (2023.eacl-main)
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| Challenge: | Socratic questioning is a form of reflective inquiry often employed in education to encourage critical thinking in students. |
| Approach: | They present a first large dataset of 110K questions, context pairs for Socratic Question Generation. |
| Outcome: | The proposed model produces realistic, type-sensitive, human-like Socratic questions . authors show that the model can be used in counseling and coaching . |
Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs (2022.naacl-main)
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| Challenge: | NLP-powered automatic question generation (QG) techniques have not been widely adopted in classrooms to date. |
| Approach: | They propose to identify key impediments and improve the usability of NLP-powered automatic question generation techniques by understanding how instructors construct questions and identifying touch points to enhance the underlying NLP models. |
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Generating Questions from Wikidata Triples (2022.lrec-1)
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| Challenge: | Existing methods for question generation from knowledge bases rely on extensive pre- and post-processing of the input triple. |
| Approach: | They revisit KBQG using pre training, a new (triple, question) dataset and taking question type into account and provide a more extended KBqg dataset. |
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Question Generation for Adaptive Education (2021.acl-short)
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| Challenge: | Existing systems depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. |
| Approach: | They propose to fine-tune pre-trained language models for deep knowledge tracing to generate reversetranslation questions conditioned on the student and target difficulty. |
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Semantic Graphs for Generating Deep Questions (2020.acl-main)
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| Challenge: | Existing research has focused on generating factoid questions relevant to one fact obtainable from a single sentence. |
| Approach: | They propose a framework that first constructs a semantic-level graph and then encodes it by introducing an attention-based GGNN. |
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SkillQG: Learning to Generate Question for Reading Comprehension Assessment (2023.findings-acl)
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| Challenge: | Existing question generation systems focus on the literal nature of questions and rarely consider comprehension types of the generated questions. |
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (2020.acl-main)
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| Challenge: | Question Generation is a simple syntactic transformation but many aspects of semantics influence what questions are good to form. |
| Approach: | They propose a set of syntactic rules which transform declarative sentences into question-answer pairs. |
| Outcome: | The proposed system generates a larger number of highly grammatical and relevant questions than existing QG systems. |
Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)
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| Challenge: | Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information. |
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