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

Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona T. Diab

Track: Main Track

Keywords: Machine Translation, AI Terminology, Domain-Specific Datasets, Hybrid LLM-Human Framework

TL;DR: GIST is a multilingual AI terminology dataset with 5K terms in five languages, created via a hybrid LLM-human approach. It ensures high quality and enables broader accessibility and inclusivity in AI research.

Abstract: The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. This work introduces GIST, a large-scale multilingual AI terminology dataset containing 5K terms extracted from top AI conference papers spanning 2000 to 2023. The terms were 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 was benchmarked against existing resources, demonstrating superior translation accuracy through crowdsourced evaluation. GIST was integrated into translation workflows using post-translation refinement methods that required no retraining, where LLM prompting consistently improved BLEU and COMET scores. A web demonstration on the ACL Anthology platform highlights its practical application, showcasing improved accessibility for non-English speakers. We address a critical gap in AI terminology resources and fosters global inclusivity and collaboration in AI research.

Submission Number: 15