Papers with QG systems
Diverse Content Selection for Educational Question Generation (2023.eacl-srw)
Copied to clipboard
| Challenge: | Current automatic Question Generation (QG) systems do not consider content selection as an educational aspect. |
| Approach: | They propose to select content based on relevance and topic diversity for question generation on educational document level. |
| Outcome: | The proposed solution reduces the time and effort required to create questions for students on educational datasets. |
Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs (2022.naacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed methods can be used by 11 instructors across 7 universities and highlight their needs and needs when creating questions. |
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (2020.acl-main)
Copied to clipboard
| 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. |
Cross-Lingual Training for Automatic Question Generation (P19-1)
Copied to clipboard
| Challenge: | Automatic question generation is a challenging problem in natural language understanding . manual curating a dataset of comparable size for a new language is tedious and expensive. |
| Approach: | They propose to reuse available large QG dataset in a secondary language to learn a QG model for a primary language. |
| Outcome: | The proposed model outperforms baseline models in Hindi and Chinese. |