Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational Applications

Kamyar Zeinalipour, Mohamed Zaky Saad, Marco Maggini, Marco Gori

TL;DR

This work introduces the first AI-powered Arabic crossword puzzle generator that leverages multiple large language models to create high-quality clue-answer pairs from input text or provided answers. It builds and employs a large Arabic clue-answer dataset, applies fine-tuning, few-/zero-shot learning, and robust validation to produce usable crossword clues and layouts, and demonstrates its effectiveness through extensive experiments. The contributions include a publicly accessible Arabic clue-answer dataset, a dual-path generation framework, classifier-based quality control, and a scalable schema generator for educational crosswords, with demonstrated potential for classroom integration and enhanced language learning. The approach advances Arabic NLP applications in educational tooling by combining AI-driven clue creation with interactive puzzle design, enabling scalable, engaging vocabulary and concept reinforcement.

Abstract

This paper presents the first Arabic crossword puzzle generator driven by advanced AI technology. Leveraging cutting-edge large language models including GPT4, GPT3-Davinci, GPT3-Curie, GPT3-Babbage, GPT3-Ada, and BERT, the system generates distinctive and challenging clues. Based on a dataset comprising over 50,000 clue-answer pairs, the generator employs fine-tuning, few/zero-shot learning strategies, and rigorous quality-checking protocols to enforce the generation of high-quality clue-answer pairs. Importantly, educational crosswords contribute to enhancing memory, expanding vocabulary, and promoting problem-solving skills, thereby augmenting the learning experience through a fun and engaging approach, reshaping the landscape of traditional learning methods. The overall system can be exploited as a powerful educational tool that amalgamates AI and innovative learning techniques, heralding a transformative era for Arabic crossword puzzles and the intersection of technology and education.

ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational Applications

TL;DR

This work introduces the first AI-powered Arabic crossword puzzle generator that leverages multiple large language models to create high-quality clue-answer pairs from input text or provided answers. It builds and employs a large Arabic clue-answer dataset, applies fine-tuning, few-/zero-shot learning, and robust validation to produce usable crossword clues and layouts, and demonstrates its effectiveness through extensive experiments. The contributions include a publicly accessible Arabic clue-answer dataset, a dual-path generation framework, classifier-based quality control, and a scalable schema generator for educational crosswords, with demonstrated potential for classroom integration and enhanced language learning. The approach advances Arabic NLP applications in educational tooling by combining AI-driven clue creation with interactive puzzle design, enabling scalable, engaging vocabulary and concept reinforcement.

Abstract

This paper presents the first Arabic crossword puzzle generator driven by advanced AI technology. Leveraging cutting-edge large language models including GPT4, GPT3-Davinci, GPT3-Curie, GPT3-Babbage, GPT3-Ada, and BERT, the system generates distinctive and challenging clues. Based on a dataset comprising over 50,000 clue-answer pairs, the generator employs fine-tuning, few/zero-shot learning strategies, and rigorous quality-checking protocols to enforce the generation of high-quality clue-answer pairs. Importantly, educational crosswords contribute to enhancing memory, expanding vocabulary, and promoting problem-solving skills, thereby augmenting the learning experience through a fun and engaging approach, reshaping the landscape of traditional learning methods. The overall system can be exploited as a powerful educational tool that amalgamates AI and innovative learning techniques, heralding a transformative era for Arabic crossword puzzles and the intersection of technology and education.
Paper Structure (20 sections, 1 equation, 4 figures, 5 tables)

This paper contains 20 sections, 1 equation, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Dataset
  4. Methodology
  5. Path (a): Generating clue-answer pairs from input text
  6. Keyword extraction
  7. Generating crossword clues from the extracted keywords
  8. Path(a) Validation
  9. Path (b): Generating clues based on provided answers
  10. Fine-tuning LLMs to generate clues from provided answers
  11. Path(b) Validation
  12. Schema Generator
  13. Experiments
  14. Experimental Evaluation: Path (a)
  15. Experimental Evaluation: Path (b)
  16. ...and 5 more sections

Figures (4)

  • Figure 1: The introduced dataset entries are visually presented in terms of answer length distribution. The blue bars represent all the clue-answer pairs, while the green bars depict the frequency of unique answers. Additionally, the red bars indicate the frequency of unique answer-clue pairs.
  • Figure 2: Overall system architecture. Path (a) Clue-answer generation from input text. Path (b) Clue generation from the given answers.
  • Figure 3: A comprehensive collection of clue-answer pairs generated by the introduced system from a given text, providing illustrative examples.
  • Figure 4: An illustrative Arabic educational crossword generated through the proposed system.