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

2024-12-24

AI Summary

To address the global research barrier caused by inaccurate AI terminology translation and scarce multilingual resources, this paper introduces GIST: the first large-scale, multilingual terminology dataset covering top-tier AI conference papers from 2000–2023 (5,000 terms in Arabic, Chinese, French, Japanese, and Russian). Methodologically, GIST employs an LLM-driven hybrid term extraction pipeline, expert validation, and prompt-engineering-based post-translation refinement—requiring no model retraining. As the first high-quality, verifiable, and open-source multilingual terminology benchmark specifically designed for AI, GIST achieves significantly higher translation accuracy than existing resources in crowdsourced evaluation. When integrated into machine translation workflows, it improves BLEU and COMET scores by +2.4 and +3.1, respectively. Furthermore, GIST has been deployed in the ACL Anthology multilingual platform, demonstrably enhancing participation and accessibility for non-English-speaking researchers.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. We introduced 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. This work aims to address critical gaps in AI terminology resources and fosters global inclusivity and collaboration in AI research.

Problem

• Machine Translation
• Domain-specific Terminology
• Language Barrier in AI Research

Innovation

• GIST Database
• Multilingual AI Terminology
• Translation Accuracy Enhancement