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

TL;DR

This paper introduces GIST, a large multilingual AI terminology dataset created through a hybrid extraction and translation process, improving translation accuracy and promoting global AI inclusivity.

Contribution

The paper presents GIST, a comprehensive multilingual AI terminology dataset with high-quality translations, and demonstrates its integration into translation workflows to enhance accessibility.

Findings

GIST outperforms existing resources in translation accuracy.

Prompt-based refinement improves BLEU and COMET scores.

Web demo showcases practical application for AI research inclusivity.

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. Our data is at https://huggingface.co/datasets/Jerry999/multilingual-terminology