  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.
