Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)

• URL: http://arxiv.org/abs/2412.18367v5
• Date: Mon, 17 Feb 2025 18:13:38 GMT
• Title: Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)
• Authors: Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona Diab,
• Abstract summary: GIST is a large-scale multilingual AI terminology dataset containing 5K terms extracted from top AI conference papers spanning 2000 to 2023. The terms are translated into Arabic, Chinese, French, Japanese, and Russian using a hybrid framework that combines LLMs for extraction with human expertise for translation. This work aims to address critical gaps in AI terminology resources and fosters global inclusivity and collaboration in AI research.
• Score: 19.91873751674613
• License:
• Abstract: The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. We introduce GIST, a large-scale multilingual AI terminology dataset containing 5K terms extracted from top AI conference papers spanning 2000 to 2023. The terms are translated into Arabic, Chinese, French, Japanese, and Russian using a hybrid framework that combines LLMs for extraction with human expertise for translation. The dataset's quality is benchmarked against existing resources, demonstrating superior translation accuracy through crowdsourced evaluation. GIST is integrated into translation workflows using post-translation refinement methods that require no retraining, where LLM prompting consistently improves BLEU and COMET scores. A web demonstration on the ACL Anthology platform highlights its practical application, showcasing improved accessibility for non-English speakers. This work aims to address critical gaps in AI terminology resources and fosters global inclusivity and collaboration in AI research.

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