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Tibetan Question Generation Based on Sequence to Sequence Model 认领 引用 被引量：1
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作者 Yuan Sun Chaofan Chen +1 位作者 Andong Chen Xiaobing Zhao 《Computers, Materials & Continua》 SCIE EI 2021年第9期3203-3213,共11页
As the dual task of question answering,question generation(QG)is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph.The QG task is of great significance to quest... As the dual task of question answering,question generation(QG)is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph.The QG task is of great significance to question answering systems,conversational systems,and machine reading comprehension systems.Recent sequence to sequence neural models have achieved outstanding performance in English and Chinese QG tasks.However,the task of Tibetan QG is rarely mentioned.The key factor impeding its development is the lack of a public Tibetan QG dataset.Faced with this challenge,the present paper first collects 425 articles from the Tibetan Wikipedia website and constructs 7,234 question–answer pairs through crowdsourcing.Next,we propose a Tibetan QG model based on the sequence to sequence framework to generate Tibetan questions from given paragraphs.Secondly,in order to generate answer-aware questions,we introduce an attention mechanism that can capture the key semantic information related to the answer.Meanwhile,we adopt a copy mechanism to copy some words in the paragraph to avoid generating unknown or rare words in the question.Finally,experiments show that our model achieves higher performance than baseline models.We also further explore the attention and copy mechanisms,and prove their effectiveness through experiments. 展开更多
关键词 Tibetan question generation copy mechanism attention
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AI-infused Semantic Model to Enrich and Expand Programming Question Generation 认领 引用 被引量：2
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作者 I-Han Hsiao Cheng-Yu Chung 《Journal of Artificial Intelligence and Technology》 2022年第2期47-54,共8页
Creating practice questions for programming learning is not easy.It requires the instructor to diligently organize heterogeneous learning resources,that is,conceptual programming concepts and procedural programming ru... Creating practice questions for programming learning is not easy.It requires the instructor to diligently organize heterogeneous learning resources,that is,conceptual programming concepts and procedural programming rules.Today’s programming question generation(PQG)is still largely relying on the demanding creation task performed by the instructors without advanced technological support.In this work,we propose a semantic PQG model that aims to help the instructor generate new programming questions and expand the assessment items.The PQG model is designed to transform conceptual and procedural programming knowledge from textbooks into a semantic network by the Local Knowledge Graph(LKG)and Abstract Syntax Tree(AST).For any given question,the model queries the established network to find related code examples and generates a set of questions by the associated LKG/AST semantic structures.We conduct analysis to compare instructor-made questions from 9 undergraduate introductory programming courses and textbook questions.The results show that the instructormade questions had much simpler complexity than the textbook ones.The disparity of topic distribution intrigued us to further research the breadth and depth of question quality and also to investigate the complexity of the questions in relation to the student performances.Finally,we report a user study results on the proposed Artificial Intelligent-infused semantic PQG model in examining the machine-generated questions’quality. 展开更多
关键词 Assessment Programming Semantic Modeling Automatic Question Generation
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Enhancing Relevance and Efficiency in Visual Question Generation Through Redundant Object Filtering 认领 引用
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作者 Feifei Xu Yingchen Zhou +2 位作者 Zheng Zhong Guangzhen Li Wang Zhou 《国际计算机前沿大会会议论文集》 2024年第3期81-95,共15页
GuessWhat?!is a goal-oriented visual dialog task where the Guesser infers the target object in an image by asking several questions,and the Answerer provides answers.The quality of question generation is vital for the... GuessWhat?!is a goal-oriented visual dialog task where the Guesser infers the target object in an image by asking several questions,and the Answerer provides answers.The quality of question generation is vital for the task,but exist-ing methods do not consider the redundant objects brought by Faster RCNN for object detection,leading to meaningless,repetitive questions.To address this,we propose Question Improvement and Redundancy Elimination(QIRE)to enhance question generation byfiltering redundant object features.To overcome the prob-lem that Faster RCNN must follow afixed number of objects,resulting in poor quality of detected objects,we design a new module for capturing visual represen-tations of variable number of objects.In addition,we put forward the Target Cate-gory Learner(TCL)module to simulate human questioning thinking,and apply a penalty mechanism to reduce repetition.Experimental results on the GuessWhat?!dataset show QIRE’s competitiveness in question quality and dialog effectiveness compared to existing methods. 展开更多
关键词 Visual Dialog Question Generation Question-NG Strategy Redundancy Elimination
HACAN:a hierarchical answer-aware and context-aware network for question generation 认领 引用 被引量：3
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作者 Ruijun SUN Hanqin TAO +1 位作者 Yanmin CHEN Qi LIU 《Frontiers of Computer Science》 SCIE EI CSCD 2024年第5期45-55,共11页
Question Generation(QG)is the task of generating questions according to the given contexts.Most of the existing methods are based on Recurrent Neural Networks(RNNs)for generating questions with passage-level input for... Question Generation(QG)is the task of generating questions according to the given contexts.Most of the existing methods are based on Recurrent Neural Networks(RNNs)for generating questions with passage-level input for providing more details,which seriously suffer from such problems as gradient vanishing and ineffective information utilization.In fact,reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education scenario.To that end,in this paper,we propose a novel Hierarchical Answer-Aware and Context-Aware Network(HACAN)to construct a high-quality passage representation and judge the balance between the sentences and the whole passage.Specifically,a Hierarchical Passage Encoder(HPE)is proposed to construct an answer-aware and context-aware passage representation,with a strategy of utilizing multi-hop reasoning.Then,we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder(HPD)which determines when to utilize the passage information.We conduct extensive experiments on the SQuAD dataset,where the results verify the effectivenesss of our model in comparison with several baselines. 展开更多
关键词 question generation natural language generation natural language processing sequence to sequence
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Automatic Generation of Amharic Math Word Problem and Equation 认领 引用
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作者 Andinet Assefa Bekele 《Journal of Computer and Communications》 2020年第8期59-77,共19页
Math word problem uses a real word story to present basic arithmetic operations using textual narration. It is used to develop student’s comprehension skill in conjunction with the ability to generate a solution that... Math word problem uses a real word story to present basic arithmetic operations using textual narration. It is used to develop student’s comprehension skill in conjunction with the ability to generate a solution that agrees with the story given in the problem. To master math word problem solving, students need to be given fresh and enormous amount of problems, which normal textbooks as well as teachers fail to provide most of the time. To fill the gap, a few research works have been proposed on techniques to automatically generate math word problems and equations mainly for English speaking community. Amharic is a Semitic language spoken by more than hundred million Ethiopians and is a language of instruction in elementary schools in Ethiopia. And yet it belongs to one of a less resourced language in the field of linguistics and natural language processing (NLP). Hence, in this paper, a strategy for automatic generation of Amharic Math Word (AMW) problem and equation is proposed, which is a first attempt to introduce the use template based shallow NLP approach to generate math word problem for Amharic language as a step towards enabling comprehension and learning problem solving in mathematics for primary school students. The proposed novel technique accepts a sample AMW problem as user input to form a template. A template provides AMW problem with placeholders, type of problem and equation template. It is used as a pattern to generate semantically equivalent AMW problems with their equations. To validate the reality of the proposed approach, a prototype was developed and used as a testing platform. Experimental results have shown 93.84% overall efficiency on the core task of forming templates from a given corpus containing AMW problems collected from elementary school mathematics textbooks and other school worksheets. Human judges have also found generated AMW problem and equation as solvable as the textbook problems. 展开更多
关键词 AMW Problem Automatic Problem Generation Template Based Shallow NLP Approach Question Generation Math Word Problem
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Leveraging Structured Information from a Passage to Generate Questions 认领 引用 被引量：2
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作者 Jian Xu Yu Sun +2 位作者 Jianhou Gan Mingtao Zhou Di Wu 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2023年第3期464-474,共11页
Question Generation(QG)is the task of utilizing Artificial Intelligence(AI)technology to generate questions that can be answered by a span of text within a given passage.Existing research on QG in the educational fiel... Question Generation(QG)is the task of utilizing Artificial Intelligence(AI)technology to generate questions that can be answered by a span of text within a given passage.Existing research on QG in the educational field struggles with two challenges:the mainstream QG models based on seq-to-seq fail to utilize the structured information from the passage;the other is the lack of specialized educational QG datasets.To address the challenges,a specialized QG dataset,reading comprehension dataset from examinations for QG(named RACE4QG),is reconstructed by applying a new answer tagging approach and a data-filtering strategy to the RACE dataset.Further,an end-to-end QG model,which can exploit the intra-and inter-sentence information to generate better questions,is proposed.In our model,the encoder utilizes a Gated Recurrent Units(GRU)network,which takes the concatenation of word embedding,answer tagging,and Graph Attention neTworks(GAT)embedding as input.The hidden states of the GRU are operated with a gated self-attention to obtain the final passage-answer representation,which will be fed to the decoder.Results show that our model outperforms baselines on automatic metrics and human evaluation.Consequently,the model improves the baseline by 0.44,1.32,and 1.34 on BLEU-4,ROUGE-L,and METEOR metrics,respectively,indicating the effectivity and reliability of our model.Its gap with human expectations also reflects the research potential. 展开更多
关键词 automatic Question Generation(QG) RACE4QG dataset Answer-Oriented GAT(AO-GAT) attention mechanism structured information
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IntelliA:an intelligent educational assignment practicing platform driven by generative AI 认领 引用 被引量：1
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作者 Zhihai Wang Kun Niu +2 位作者 Siying Liu Shuai Liu Fangbo Wu 《International Journal of Intelligent Computing and Cybernetics》 EI 2025年第4期661-675,共15页
Purpose–Educational assignments are pivotal for student learning,yet educators face the dual burden of creating meaningful questions and providing timely,individualized feedback.This study introduces IntelliA,a gener... Purpose–Educational assignments are pivotal for student learning,yet educators face the dual burden of creating meaningful questions and providing timely,individualized feedback.This study introduces IntelliA,a generative AI-powered platform designed to address these challenges by holistically automating the entire assignment lifecycle,from pedagogically aware question generation to concept-centric evaluation,thereby alleviating instructor workload and enhancing student learning outcomes.Design/methodology/approach–IntelliA’s core innovation is a synergistic architecture that operationalizes pedagogical principles directly into the AI-driven assignment workflow.For question generation,its pedagogical primitive-driven system allows educators to design assessments at a conceptual level–by selecting instructional goals–rather than manually crafting prompts.For evaluation,a novel three-stage,rubric-driven methodology is designed to produce pedagogically valuable feedback,grounding its supportive guidance in objective,evidence-based scoring.These subsystems are unified by a unified quality augmentation module,creating a self-reinforcing loop that continuously refines the system’s pedagogical alignment.This architecture’s ability to enhance both educational quality and operational efficiency was rigorously validated through a semester-long university deployment,assessed via a multi-faceted framework of instructor interviews,student surveys and cross-model benchmarking.Findings–The results demonstrate that IntelliA significantly reduces assignment creation time for instructors while maintaining high pedagogical quality in generated questions.Student surveys revealed high satisfaction with the grading accuracy and the personalized,actionable feedback,which they reported as instrumental to their learning.The findings confirm that an architecturally sophisticated AI system can successfully move beyond simple automation to enhance both operational efficiency and educational quality.Research limitations/implications–Our study’s validation was conducted within a single university STEM course,which limits the generalizability of our findings.Future research should expand the library of pedagogical primitives to diverse domains(e.g.humanities and K-12)and validate their effectiveness in these new contexts.Additionally,a more rigorous,controlled experiment is needed to quantitatively measure efficiency gains in terms of time-on-task and cognitive load.This work signals a shift in AI-in-education research,moving from single-task automation toward designing synergistic,architecturally aware systems.Practical implications–IntelliA offers a practical,scalable solution for educators to significantly reduce the workload associated with assignment creation and feedback personalization.By automating routine tasks,the platform empowers instructors to shift their focus from content generation to higher-value pedagogical activities,such as refining learning objectives and providing nuanced student support.This model demonstrates how AI can be integrated into educational workflows as a collaborative partner,enhancing both operational efficiency and the quality of pedagogical practice.Social implications–The widespread adoption of synergistic AI systems like IntelliA could democratize access to high-quality,personalized learning support,potentially narrowing educational equity gaps.However,it also raises critical social questions about the future of the teaching profession and the risk of over-reliance on automation.The key social implication is the need for a public discourse on responsible AI integration in education,ensuring that technology serves to amplify educator agency and enhance human-centric learning,rather than deskilling teachers or creating uniform,passive learning pathways.Originality/value–This study presents a significant leap forward by proposing a novel,synergistic system architecture for AI in education.Unlike prior systems that address isolated tasks,IntelliA offers a holistically integrated and self-improving ecosystem.By demonstrating how to embed pedagogical principles directly into an AI’s core logic,IntelliA provides a scalable and flexible blueprint for a new generation of educational tools.This approach moves beyond simple automation,fostering a new dynamic where AI functions not as a mere instrument but as a genuine partner in the pedagogical process. 展开更多
关键词 Educational technology Generative AI Question generation Short answer scoring Feedback generation
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