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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： alhazmi, Elaf Sheng, Quan Z. Zhang, Wei Emma Zaib, Munazza alhazmi, Ahoud School of Computing Macquarie University Australia School of Computer and Mathematical Sciences The University of Adelaide Australia College of Engineering and Computing in Al-Lith Umm Al-Qura University Saudi Arabia

ISBN: (纸本)9798891761643

The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions. This task is widely utilized in educational settings across various domains and subjects. The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options. The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models. This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors. This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both text-based and multi-modal domains. It also evaluates existing AI models and benchmarks and discusses potential future research directions. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation

arXiv

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arXiv 2024年

The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions. This task is widely utilized in educational settings across various domains and subjects. The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options. The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models. This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors. This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both text-based and multi-modal domains. It also evaluates existing AI models and benchmarks and discusses potential future research *** Codes Computation and Language (***) Copyright © 2024, The Authors. all rights reserved.

关键词： Deep learning

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CEIoT: A framework for interlinking smart things in the internet of things 12th

CEIoT: A framework for interlinking smart things in the inte...

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12th International Conference on Advanced Data Mining and Applications, ADMA 2016

作者： Shemshadi, ali Sheng, Quan Z. Qin, Yongrui alzubaidi, ali School of Computer Science The University of Adelaide AdelaideSA5005 Australia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom College of Computer Science - Al Lith Umm Al-Qura University Mecca Saudi Arabia

ISBN: (纸本)9783319495859

In the emerging Internet of Things (IoT) environment, things are interconnected but not interlinked. Interlinking relevant things offers great opportunities to discover implicit relationships and enable potential interactions among things. To achieve this goal, implicit correlations between things need to be discovered. However, little work has been done on this important direction and the lack of correlation discovery has inevitably limited the power of interlinking things in IoT. With the rapidly growing number of things that are connected to the Internet, there are increasing needs for correlations formation and discovery so as to support interlinking relevant things together effectively. In this paper, we propose a novel approach based on Multi-Agent Systems (MAS) architecture to extract correlations between smart things. Our MAS system is able to identify correlations on demand due to the autonomous behaviors of object agents. Specifically, we introduce a novel open-sourced framework, namely CEIoT, to extract correlations in the context of IoT. Based on the attributes of things our IoT dataset, we identify three types of correlations in our system and propose a new approach to extract and represent the correlations between *** implement our architecture using Java Agent Development Framework (JADE) and conduct experimental studies on both synthetic and real-world datasets. The results demonstrate that our approach can extract the correlations at a much higher speed than the naive pairwise computation method. © Springer International Publishing AG 2016.

关键词： Internet of things

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