Recent Advances in Neural Question Generation
arXiv:1905.08949
Abstract
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a comprehensive survey of neural question generation, examining the corpora, methodologies, and evaluation methods. From this, we elaborate on what we see as emerging on NQG's trend: in terms of the learning paradigms, input modalities, and cognitive levels considered by NQG. We end by pointing out the potential directions ahead.
Survey of neural question generation
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- A Dataset and Baselines for Visual Question Answering on Art
- Towards Automatic Generation of Questions from Long Answers
- I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection
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- Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen
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- Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
- PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation
- Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations
- Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting
- AGenT Zero: Zero-shot Automatic Multiple-Choice Question Generation for Skill Assessments
- MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training
- Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables