arXiv 2508.035292025-11-02cs.CL

Mafoko：面向南非NLP的开放多语言术语体系的结构化与构建

Mafoko: Structuring and Building Open Multilingual Terminologies for South African NLP

Data Science for Social Impact, Dept. of Computer Science, University of Pretoria（南非普里尼亚大学计算机科学系数据科学与社会影响研究中心）；AfriDSAI, University of Pretoria（南非普里尼亚大学非洲数据科学与人工智能研究所）；Lelapa AI（莱拉帕人工智能）；Economics and Management Sciences, University of the Free State（南非自由州大学经济与管理科学系）；Interdisciplinary Centre for Digital Futures, University of the Free State（南非自由州大学跨学科数字未来研究中心）

Vukosi Marivate, Isheanesu Dzingirai, Fiskani Banda, Richard Lastrucci, Thapelo Sindane, Keabetswe Madumo, Kayode Olaleye, Abiodun Modupe, Unarine Netshifhefhe,… 展开作者

Vukosi Marivate, Isheanesu Dzingirai, Fiskani Banda, Richard Lastrucci, Thapelo Sindane, Keabetswe Madumo, Kayode Olaleye, Abiodun Modupe, Unarine Netshifhefhe, Herkulaas Combrink, Mohlatlego Nakeng, Matome Ledwaba

备注：: Accepted for Sixth Workshop on Resources for African Indigenous Languages (RAIL) 2025

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对南非官方语言术语数据碎片化且非机器可读的问题，本文构建了开放的多语言术语数据集Mafoko，并通过RAG流水线显著提升了英译Tshivenda机器翻译的准确率与一致性。

摘要：

尽管存在大量政府和学术术语表，但南非官方语言结构化术语数据的严重匮乏阻碍了多语言NLP的发展。这些宝贵资源处于碎片化状态且受限于非机器可读格式，无法用于计算研究与开发。Mafoko通过系统性地聚合、清洗和标准化这些分散资源，将其转化为开放、可互操作的数据集来解决此问题。我们发布了基于公平且以非洲为中心的NOODL框架的基础Mafoko数据集。为展示其即时效用，我们将该术语整合到检索增强生成（RAG）流水线中。实验表明，大语言模型在英译Tshivenda机器翻译的准确率和领域特定一致性方面获得了显著提升。Mafoko为开发稳健且公平的NLP技术提供了可扩展的基础，确保南非丰富的语言多样性在数字时代得到体现。

英文摘要：

The critical lack of structured terminological data for South Africa's official languages hampers progress in multilingual NLP, despite the existence of numerous government and academic terminology lists. These valuable assets remain fragmented and locked in non-machine-readable formats, rendering them unusable for computational research and development. Mafoko addresses this challenge by systematically aggregating, cleaning, and standardising these scattered resources into open, interoperable datasets. We introduce the foundational Mafoko dataset, released under the equitable, Africa-centered NOODL framework. To demonstrate its immediate utility, we integrate the terminology into a Retrieval-Augmented Generation (RAG) pipeline. Experiments show substantial improvements in the accuracy and domain-specific consistency of English-to-Tshivenda machine translation for large language models. Mafoko provides a scalable foundation for developing robust and equitable NLP technologies, ensuring South Africa's rich linguistic diversity is represented in the digital age.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。