| 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. |
Similar Papers
Diversifying Question Generation over Knowledge Base via External Natural Questions (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods on knowledge base question generation focus on refining the quality of a single generated question. |
| Approach: | They propose a new diversity evaluation metric which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. |
| Outcome: | The proposed model outperforms pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT. |
On the Importance of Diversity in Question Generation for QA (2020.acl-main)
Copied to clipboard
| Challenge: | In this paper, we show that textual diversity in automatic question generation is beneficial for downstream QA. |
| Approach: | They propose to use textual diversity to promote automatic question generation as a quality measure for QA. |
| Outcome: | The proposed measure of QG quality correlates well with evaluation on QA. |
Generating Highly Relevant Questions (D19-1)
Copied to clipboard
| Challenge: | Existing neural QG models generate generic questions that are not relevant to passages and answers. |
| Approach: | They propose to prioritize words that are morphologically close to words in the passage when generating questions. |
| Outcome: | The proposed methods improve relevance of generated questions to passages and answers. |
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. |
KHANQ: A Dataset for Generating Deep Questions in Education (2022.coling-1)
Copied to clipboard
| 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. |
Improving Question Generation with Multi-level Content Planning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies suggest key phrase selection is essential for question generation, yet it is difficult to connect disjointed phrases into meaningful questions, especially for long context. |
| Approach: | They propose a QG framework that uses multi-level content planning to generate questions from a given context and an answer. |
| Outcome: | The proposed framework outperforms baselines on two popular QG datasets. |
Integrating Question Classification and Deep Learning for improved Answer Selection (C18-1)
Copied to clipboard
| Challenge: | Question Answering (QA) is the task of automatically generating answers to questions posed in natural language. |
| Approach: | They propose a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer selection. |
| Outcome: | The proposed system outperforms the current state of the art in all variations except one . the proposed system improves QA by reducing the search space of potential answers . |
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to generate questions based on answers and relevant contexts are not suitable for all questions . |
| Approach: | They propose a method to generate questions from a given answer and its relevant context. |
| Outcome: | The proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches. |
Exploring Question-Specific Rewards for Generating Deep Questions (2020.coling-main)
Copied to clipboard
| 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. |
Evaluation of Question Generation Needs More References (2023.findings-acl)
Copied to clipboard
Shinhyeok Oh, Hyojun Go, Hyeongdon Moon, Yunsung Lee, Myeongho Jeong, Hyun Seung Lee, Seungtaek Choi
| Challenge: | Existing evaluations of QG methods rely on single reference-based similarity metrics . multiple (pseudo) references are more effective for QG evaluation . |
| Approach: | They propose to paraphrase the reference question for a more robust QG evaluation. |
| Outcome: | The proposed frameworks show higher correlation with human evaluations than evaluation with a single reference. |