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

ACL ARR 2025 February Submission2836 Authors

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.

Paper Type: Long

Research Area: Machine Translation

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

Contribution Types: Data resources

Languages Studied: English, Arabic, Chinese, French, Japanese, Russian

Submission Number: 2836