Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate educational questions of fairytales or storybooks are difficult to implement due to adults lacking the skills or time to integrate such interactive opportunities. |
| Approach: | They propose a question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. |
| Outcome: | The proposed method performs well on automatic and human evaluation metrics on a newly proposed educational question-answering dataset FairytaleQA. |
Similar Papers
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books (2022.acl-long)
Copied to clipboard
| Challenge: | Existing question answering (QA) techniques are created mainly to answer questions asked by humans, but in educational applications, teachers often need to decide what questions to ask . |
| Approach: | They propose to use a fairytale-themed storybook as input to generate QA pairs that can test a student's comprehension skills. |
| Outcome: | The proposed system outperforms state-of-the-art QAG baseline systems and builds an interactive story-telling application for the future real-world deployment. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
Copied to clipboard
Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
A Feasibility Study of Answer-Agnostic Question Generation for Education (2022.findings-acl)
Copied to clipboard
Liam Dugan, Eleni Miltsakaki, Shriyash Upadhyay, Etan Ginsberg, Hannah Gonzalez, DaHyeon Choi, Chuning Yuan, Chris Callison-Burch
| Challenge: | a feasibility study into the applicability of answer-agnostic question generation models to textbook passages is conducted . a significant portion of errors arise from asking irrelevant or un-interpretable questions, a study finds . |
| Approach: | They conduct a feasibility study into the applicability of answer-agnostic question generation models to textbook passages. |
| Outcome: | The proposed model reduces the time it takes to write questions that target salient concepts . the proposed model would help professors write quizzes faster and help students stay engaged . |
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning (2021.emnlp-main)
Copied to clipboard
| Challenge: | a proposed model for question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers is based on suggested question generation in conversational news recommendation systems. |
| Approach: | They propose a model for generating question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers. |
| Outcome: | The proposed model captures the central gists of the articles and achieves high answer accuracy. |
Diversity Enhanced Narrative Question Generation for Storybooks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Question generation (QG) from a given context can enhance comprehension, engagement, assessment, and overall efficacy in learning or conversational environments. |
| Approach: | They propose a multi-question generation model which generates multiple, diverse questions by focusing on context and questions. |
| Outcome: | The proposed model shows promising results across evaluation metrics, among strong baselines. |
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods for summarizing long questions are difficult due to the lack of training data and the complexity of the related subtasks. |
| Approach: | They propose a reinforcement learning-based framework for abstractive question summarization that rewards question-type identification and question-focus recognition for regularizing the question generation model. |
| Outcome: | The proposed method achieves higher performance over state-of-the-art models on two benchmark datasets. |
Event Extraction as Question Generation and Answering (2023.acl-short)
Copied to clipboard
| Challenge: | Recent work on Event Extraction addresses the error propagation issue found in token-based classification approaches. |
| Approach: | They propose a Question Generation (QG) model that generates questions that leverage contextual information instead of fixed templates. |
| Outcome: | The proposed model outperforms all previous single-task-based models on the ACE05 English dataset. |
Question Generation for Adaptive Education (2021.acl-short)
Copied to clipboard
| 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. |
| Outcome: | The proposed model can generate well-calibrated language translation questions for second language learners from a real online education platform. |
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 Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
| Approach: | They propose a method which uses questions generated heuristically from news summaries as a source of training data for a QG system. |
| Outcome: | The proposed method outperforms previous unsupervised models on three in-domain datasets and three out-of-domain ones. |