학술논문

Building Bridges of Knowledge: Innovating Education with Automated Crossword Generation
Document Type
Conference
Source
2023 International Conference on Machine Learning and Applications (ICMLA) ICMLA Machine Learning and Applications (ICMLA), 2023 International Conference on. :1228-1236 Dec, 2023
Subject
Computing and Processing
Engineering Profession
Robotics and Control Systems
Signal Processing and Analysis
Vocabulary
Zero-shot learning
Education
Layout
Generators
Natural language processing
Classification algorithms
Natural Language Processing
Educational Crossword
Large Language Models
Zero-shot shot Learning
Few-Shot Learning
Language
ISSN
1946-0759
Abstract
Educational crossword puzzles enhance critical thinking, vocabulary development, and concept reinforcement. They encourage independent learning, improve memorization, and foster problem-solving skills. With their multisensory approach, crossword puzzles offer a valuable educational experience. With the help of AI technology, creating high-quality, diverse crosswords is now easier, promoting enjoyable and effective learning experiences. In this endeavor, we harnessed the power of multiple language models, including GPT3, GPT2-XL, and BERT, to construct a comprehensive system that generates and verifies crossword clues. Our ultimate aim is to employ this system in the creation of educational crosswords. To achieve this, we compiled an extensive dataset consisting of over seven million clue-answer pairs spanning the years 1913 to mid-2021. By leveraging this dataset, we aimed to generate original yet challenging clues that engage solvers. Our generator underwent fine-tuning using this large collection of clues and corresponding answers, covering a wide range of themes. Additionally, we implemented a few/zero-shot learning techniques, such as prompt engineering, to generate clues based on given texts. To guarantee the quality of the generated clue-answer pairs, we utilized diverse classifiers, by fine-tuning pre-existing language models on a labeled dataset and additionally, we harnessed the power of the zero-shot learning approach to validate the generated clue-answer pairs effectively. This classifier effectively filters out nonsensical or subpar pairings. The evaluation results are highly encouraging, reinforcing the efficacy of the proposed approach.