Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles
Authors: Kamyar Zeinalipour, Tommaso Iaquinta, Asya Zanollo, Giovanni Angelini, Leonardo Rigutini, Marco Maggini, Marco Gori (Università di Siena, expert.ai, 2023)
Topic: Language Models

Abstract
Educational crosswords offer numerous benefits for students, including increased engagement, improved understanding, critical thinking, and memory retention. Creating high-quality educational crosswords can be challenging, but recent advances in natural language processing and machine learning have made it possible to use language models to generate nice wordplays. The exploitation of cutting-edge language models like GPT3-DaVinci, GPT3-Curie, GPT3-Babbage, GPT3-Ada, and BERT-uncased has led to the development of a comprehensive system for generating and verifying crossword clues. A large dataset of clue-answer pairs was compiled to fine-tune the models in a supervised manner to generate original and challenging clues from a given keyword. On the other hand, for generating crossword clues from a given text, Zero/Few-shot learning techniques were used to extract clues from the input text, adding variety and creativity to the puzzles. We employed the fine-tuned model to generate data and labeled the acceptability of clue-answer parts with human supervision. To ensure quality, we developed a classifier by fine-tuning existing language models on the labeled dataset. Conversely, to assess the quality of clues generated from the given text using zero/few-shot learning, we employed a zero-shot learning approach to check the quality of generated clues. The results of the evaluation have been very promising, demonstrating the effectiveness of the approach in creating high-standard educational crosswords that offer students engaging and rewarding learning experiences.

Introduction
In this paper, we present various contributions, including the introduction of a substantial dataset for Italian clue-answer pairs, we developed an innovative system using Large Language Models to generate educational crossword puzzles from given texts or answers. Our approach combines human supervision and specific guidelines to ensure high-quality and relevant clues. Our system includes a keyword extraction component (79.73% high-quality keywords) and a crossword clue generation component (76.6% relevant and acceptable clues). A validation component filters out unacceptable pairs, achieving a 69.72% detection rate. We conducted an in-depth investigation of fine-tuned generators and classifiers to enhance the quality of clues. Among the models tested, GPT3-Davinci demonstrated exceptional performance in generating clues based on given keywords, producing a remarkable 60.1% of acceptable clues. Moreover, GPT3-Davinci proved to be the most proficient classifier, accurately distinguishing between good clue-answer pairs and unacceptable ones with an impressive 79.88% accuracy. Our algorithm for generating educational crossword schemes is efficient and produces diverse layouts. This study aims to enhance student skills and promote interactive learning. Educators can integrate our system into their instruction for more effective teaching practices. Future research involves developing advanced models for direct clue-answer pair generation and exploring specialized models for different clue types. Our vision is to revolutionize educational crossword generation and unlock new innovations in teaching practice.

Conclusion