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 expe- rience. 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.

Zeinalipour, K., Iaquinta, T., Angelini, G., Rigutini, L., Maggini, M., Gori, M. (2023). Building Bridges of Knowledge: Innovating Education with Automated Crossword Generation. In 2023 International Conference on Machine Learning and Applications (ICMLA) (pp.1228-1236). New York : IEEE [10.1109/ICMLA58977.2023.00185].

Building Bridges of Knowledge: Innovating Education with Automated Crossword Generation

Kamyar Zeinalipour
Software
;
Tommaso Iaquinta
Software
;
Marco Maggini
Supervision
;
Marco Gori
Membro del Collaboration Group
2023-01-01

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 expe- rience. 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.
2023
Zeinalipour, K., Iaquinta, T., Angelini, G., Rigutini, L., Maggini, M., Gori, M. (2023). Building Bridges of Knowledge: Innovating Education with Automated Crossword Generation. In 2023 International Conference on Machine Learning and Applications (ICMLA) (pp.1228-1236). New York : IEEE [10.1109/ICMLA58977.2023.00185].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1255436