Relevant bibliographies by topics / Automated Multiple-Choice Question Answering

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Automated Multiple-Choice Question Answering'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Automated Multiple-Choice Question Answering"

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Amreen Kaur, Nishi and Beant Kaur. "A Review on Hindi Question Answering System." International Journal for Modern Trends in Science and Technology 7, no. 07 (2022): 181–85. http://dx.doi.org/10.46501/ijmtst051189.

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With advancement in technology, the process of question answering is the main field of research of text mining. In India, the natural language of people is mainly Hindi. In automatic question generation the system generate multiple choice questions automatically from Hindi text using question generation techniques. There are various approaches that can be used to generate the questions from a given text. This paper presents the review to various question generation techniques.
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Bui, Phat Tien, Hieu Chi Tran, and Thanh Huu Duong. "An approach to automatic answering for English reading comprehension tests." HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY 14, no. 2 (2024): 58–67. http://dx.doi.org/10.46223/hcmcoujs.tech.en.14.2.2917.2024.

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This study focuses on the reading comprehension problem with multiple-choice answers, using the BERT model to achieve the highest performance. The ultimate goal is to create a solution to help solve reading comprehension problems without any reasoning or knowledge, suitable for the level of students in grades six and seven. The model will solve factoid questions from a given text. Our research topic will use a deep learning model-based approach to create a model that automatically answers the English reading comprehension question. We obtain promising results to give an accuracy of 78 percent.
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Parikh, Soham, Quaizar Vohra, and Mitul Tiwari. "Automated Utterance Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13344–49. http://dx.doi.org/10.1609/aaai.v34i08.7047.

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Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrieving the right answer by a conversational assistant. Hence, utterance generation has become an important problem with the goal of generating relevant utterances (sentences or phrases) from a knowledge base article that consists of a title and a description. However, generating good utterances usually requires a lot of manual effort, creating the need
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Aarti P. Raut. "Enhancing Paraphrase Evaluation in Marathi Question Answering Systems Using Similarity Techniques." Journal of Information Systems Engineering and Management 10, no. 38s (2025): 709–18. https://doi.org/10.52783/jisem.v10i38s.6958.

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In Natural Language Processing applications, paraphrasing is essential, especially in Question Answering (QA) systems where users may provide distinct yet valid responses to the same questions. This study presents a structured approach for evaluating paraphrased answers in the Marathi language using multiple similarity measures, including Levenshtein Distance, Jaccard Similarity, and Cosine Similarity. The proposed methodology integrates one-to-one word matching, masking techniques, synonym dictionary verification, and dependency parsing to ensure grammatical and syntactic consistency. A datas
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Lee, Seongyun, Hyunjae Kim, and Jaewoo Kang. "LIQUID: A Framework for List Question Answering Dataset Generation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13014–24. http://dx.doi.org/10.1609/aaai.v37i11.26529.

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Question answering (QA) models often rely on large-scale training datasets, which necessitates the development of a data generation framework to reduce the cost of manual annotations. Although several recent studies have aimed to generate synthetic questions with single-span answers, no study has been conducted on the creation of list questions with multiple, non-contiguous spans as answers. To address this gap, we propose LIQUID, an automated framework for generating list QA datasets from unlabeled corpora. We first convert a passage from Wikipedia or PubMed into a summary and extract named e
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Kim, Juntae, Yoonseok Heo, Hogeon Yu, and Jongho Nang. "A Multi-Modal Story Generation Framework with AI-Driven Storyline Guidance." Electronics 12, no. 6 (2023): 1289. http://dx.doi.org/10.3390/electronics12061289.

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An automatic story generation system continuously generates stories with a natural plot. The major challenge of automatic story generation is to maintain coherence between consecutive generated stories without the need for human intervention. To address this, we propose a novel multi-modal story generation framework that includes automated storyline decision-making capabilities. Our framework consists of three independent models: a transformer encoder-based storyline guidance model, which predicts a storyline using a multiple-choice question-answering problem; a transformer decoder-based story
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Zhu, He, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation." Sensors 23, no. 3 (2023): 1057. http://dx.doi.org/10.3390/s23031057.

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Auxiliary clinical diagnosis has been researched to solve unevenly and insufficiently distributed clinical resources. However, auxiliary diagnosis is still dominated by human physicians, and how to make intelligent systems more involved in the diagnosis process is gradually becoming a concern. An interactive automated clinical diagnosis with a question-answering system and a question generation system can capture a patient’s conditions from multiple perspectives with less physician involvement by asking different questions to drive and guide the diagnosis. This clinical diagnosis process requi
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Emerson, John, and Yllias Chali. "Transformer-Based Multi-Hop Question Generation (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16206–7. http://dx.doi.org/10.1609/aaai.v37i13.26963.

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Question generation is the parallel task of question answering, where given an input context and, optionally, an answer, the goal is to generate a relevant and fluent natural language question. Although recent works on question generation have experienced success by utilizing sequence-to-sequence models, there is a need for question generation models to handle increasingly complex input contexts to produce increasingly detailed questions. Multi-hop question generation is a more challenging task that aims to generate questions by connecting multiple facts from multiple input contexts. In this w
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Sieke, Scott A., Betsy B. McIntosh, Matthew M. Steele, and Jennifer K. Knight. "Characterizing Students’ Ideas about the Effects of a Mutation in a Noncoding Region of DNA." CBE—Life Sciences Education 18, no. 2 (2019): ar18. http://dx.doi.org/10.1187/cbe.18-09-0173.

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Understanding student ideas in large-enrollment biology courses can be challenging, because easy-to-administer multiple-choice questions frequently do not fully capture the diversity of student ideas. As part of the Automated Analysis of Constructed Responses (AACR) project, we designed a question prompting students to describe the possible effects of a mutation in a noncoding region of DNA. We characterized answers from 1127 students enrolled in eight different large-enrollment introductory biology courses at three different institutions over five semesters and generated an analytic scoring s
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Lim, Youngsun, Hojun Choi, and Hyunjung Shim. "Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26290–98. https://doi.org/10.1609/aaai.v39i25.34827.

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Despite the impressive success of text-to-image (TTI) models, existing studies overlook the issue of whether these models accurately convey factual information. In this paper, we focus on the problem of image hallucination, where images created by TTI models fail to faithfully depict factual content. To address this, we introduce I-HallA (Image Hallucination evaluation with Question Answering), a novel automated evaluation metric that measures the factuality of generated images through visual question answering (VQA). We also introduce I-HallA v1.0, a curated benchmark dataset for this purpose
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Dissertations / Theses on the topic "Automated Multiple-Choice Question Answering"

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Silveira, Igor Cataneo. "Solving University entrance assessment using information retrieval." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-04112018-225438/.

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Answering questions posed in natural language is a key task in Artificial Intelligence. However, producing a successful Question Answering (QA) system is challenging, since it requires text understanding, information retrieval, information extraction and text production. This task is made even harder by the difficulties in collecting reliable datasets and in evaluating techniques, two pivotal points for machine learning approaches. This has led many researchers to focus on Multiple-Choice Question Answering (MCQA), a special case of QA where systems must select the correct answers from a small
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Luger, Sarah Kaitlin Kelly. "Algorithms for assessing the quality and difficulty of multiple choice exam questions." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20986.

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Multiple Choice Questions (MCQs) have long been the backbone of standardized testing in academia and industry. Correspondingly, there is a constant need for the authors of MCQs to write and refine new questions for new versions of standardized tests as well as to support measuring performance in the emerging massive open online courses, (MOOCs). Research that explores what makes a question difficult, or what questions distinguish higher-performing students from lower-performing students can aid in the creation of the next generation of teaching and evaluation tools. In the automated MCQ answer
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Book chapters on the topic "Automated Multiple-Choice Question Answering"

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Vo, Dinh-Huy, Anh-Khoa Do-Vo, Tram-Anh Nguyen-Thi, and Huu-Thanh Duong. "An Approach for Multiple Choice Question Answering System." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92942-8_7.

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Gilal, Abdul Rehman, Ahmad Waqas, Bandeh Ali Talpur, Rizwan Ali Abro, Jafreezal Jaafar, and Zaira Hassan Amur. "Question Guru: An Automated Multiple-Choice Question Generation System." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20429-6_46.

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Yang, Wangli, Jie Yang, Wanqing Li, and Yi Guo. "ConClue: Conditional Clue Extraction for Multiple Choice Question Answering." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70552-6_11.

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Cracco, Emiel. "Automatic Imitation of Multiple Agents." In Automatic Imitation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62634-0_10.

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AbstractDespite the vast and growing literature on automatic imitation, only little is known about automatic imitation in situations with multiple agents. Instead, the large majority of research focuses on dyadic interactions, where one person interacts with a single other person. This is surprising, because such dyadic interactions make up only a small part of social life. In this chapter, I will summarize recent research on automatic imitation beyond the dyad. More specifically, I will discuss four questions: Does automatic imitation change as a function of group size? What happens when indi
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Vaidya, Kedar P., Sanya A. Chetwani, and Mansi A. Radke. "Closed-Domain Multiple-Choice Question Answering System for Science Questions." In Proceedings of Seventh International Congress on Information and Communication Technology. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1607-6_65.

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Nicula, Bogdan, Stefan Ruseti, and Traian Rebedea. "Improving Deep Learning for Multiple Choice Question Answering with Candidate Contexts." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76941-7_62.

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Awadallah, Rawia, and Andreas Rauber. "Web-Based Multiple Choice Question Answering for English and Arabic Questions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11735106_54.

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Pranav, M., Gerard Deepak, and A. Santhanavijayan. "Automated Multiple-Choice Question Creation Using Synonymization and Factual Confirmation." In Advances in Data Computing, Communication and Security. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8403-6_24.

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Martinez-Gil, Jorge, Bernhard Freudenthaler, and A. Min Tjoa. "Multiple Choice Question Answering in the Legal Domain Using Reinforced Co-occurrence." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27615-7_10.

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Tran, Gia-Nghia, Duc-Tuan Luu, and Dang-Van Thin. "Exploring Visual Multiple-Choice Question Answering with Pre-trained Vision-Language Models." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2641-0_22.

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Conference papers on the topic "Automated Multiple-Choice Question Answering"

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Yang, Dongyu, Wenqing Deng, Zhe Wang, Kewen Wang, Zhiqiang Zhuang, and Hao Li. "Learning Choice Nuance for Multiple-Choice Commonsense Question Answering." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651121.

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Wei, Yaqi, and Le Li. "Enhancing Multiple-Choice Question Answering with External Knowledge and Lightweight Model Optimization." In 2024 5th International Conference on Information Science and Education (ICISE-IE). IEEE, 2024. https://doi.org/10.1109/icise-ie64355.2024.11025434.

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Luo, Shang-Bao, Hung-Shin Lee, Kuan-Yu Chen, and Hsin-Min Wang. "Spoken Multiple-Choice Question Answering Using Multimodal Convolutional Neural Networks." In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019. http://dx.doi.org/10.1109/asru46091.2019.9003966.

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Gabín, Jorge, Anxo Pérez, and Javier Parapar. "Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation." In XoveTIC Conference. MDPI, 2021. http://dx.doi.org/10.3390/engproc2021007023.

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Gabín, Jorge, Anxo Pérez, and Javier Parapar. "Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation." In XoveTIC Conference. MDPI, 2021. http://dx.doi.org/10.3390/engproc2021007023.

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Madoo, Raaheem, and Varsha Hole. "Automated Multi-Choice Question Answering System using Natural Language Processing." In 2024 3rd International Conference for Innovation in Technology (INOCON). IEEE, 2024. http://dx.doi.org/10.1109/inocon60754.2024.10511814.

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Sreekantan, Jaijith, Chad Hutchison, and Pratuat Amatya. "Expert System for Question Answering on Anomalous Events and Mitigation Strategies Using Bidirectional Transformers and Knowledge Graphs." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211855-ms.

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Abstract Daily drilling reports provide vital information for well planning as they capture anomalous events and mitigation measures during drilling operations. Previous works predominantly focus on search frameworks for information retrieval from these reports. However, the context between searches is lost, preventing users from narrowing down to the exact answer. Here, we present a transformer-based closed domain conversational agent for longer dialogues to guide users to contextual information for anomalous drilling events through natural language. Automated text extraction, cleaning and va
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Chaturvedi, Akshay, Onkar Pandit, and Utpal Garain. "CNN for Text-Based Multiple Choice Question Answering." In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-2044.

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Dalal, Dhairya, Mihael Arcan, and Paul Buitelaar. "Enhancing Multiple-Choice Question Answering with Causal Knowledge." In Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.deelio-1.8.

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Roy, Shishir, Nayeem Ehtesham, Md Saiful Islam, and Marium-E-Jannat. "Augmenting BERT with CNN for Multiple Choice Question Answering." In 2021 24th International Conference on Computer and Information Technology (ICCIT). IEEE, 2021. http://dx.doi.org/10.1109/iccit54785.2021.9689877.

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Reports on the topic "Automated Multiple-Choice Question Answering"

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Mintii, Iryna S., Svitlana V. Shokaliuk, Tetiana A. Vakaliuk, Mykhailo M. Mintii, and Vladimir N. Soloviev. Import test questions into Moodle LMS. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3271.

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The purpose of the study is to highlight the theoretical and methodological aspects of preparing the test questions of the most common types in the form of text files for further import into learning management system (LMS) Moodle. The subject of the research is the automated filling of the Moodle LMS test database. The objectives of the study: to analyze the import files of test questions, their advantages and disadvantages; to develop guidelines for the preparation of test questions of common types in the form of text files for further import into Moodle LMS. The action algorithms for import
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