开发一种英语-埃菲克语语料库和机器翻译系统以促进数字化包容
Developing an English-Efik Corpus and Machine Translation System for Digitization Inclusion
AI总结：
本文评估了最先进的多语言神经机器翻译模型在英语-埃菲克语翻译中的有效性，利用了一个小型社区整理的平行语料库，展示了NLLB200模型在低资源语言机器翻译中的可行性。
摘要：
低资源语言是人类历史的重要宝库，保存着文化和智力多样性。尽管其重要性，它们在现代自然语言处理系统中仍然被忽视。尽管在广泛使用的非洲语言如斯瓦希里语、约鲁巴语和阿姆哈拉语上取得了进展，较小的原生语言如埃菲克语在机器翻译研究中仍然被低估。本研究评估了最先进的多语言神经机器翻译模型在英语-埃菲克语翻译中的有效性，利用了一个小型社区整理的平行语料库，包含13,865个句子对。我们在此数据集上微调了mT5多语言模型和NLLB200模型。NLLB-200在mT5之上表现更优，分别在英语-埃菲克语和埃菲克语-英语翻译中取得了BLEU分数26.64和31.21，对应的chrF分数为51.04和47.92，表明了更流畅和语义忠实的表现。我们的发现证明了开发低资源语言实用机器翻译工具的可行性，并突显了包容性数据实践和文化根基评估在推进公平NLP中的重要性。
英文摘要：
Low-resource languages serve as invaluable repositories of human history, preserving cultural and intellectual diversity. Despite their significance, they remain largely absent from modern natural language processing systems. While progress has been made for widely spoken African languages such as Swahili, Yoruba, and Amharic, smaller indigenous languages like Efik continue to be underrepresented in machine translation research. This study evaluates the effectiveness of state-of-the-art multilingual neural machine translation models for English-Efik translation, leveraging a small-scale, community-curated parallel corpus of 13,865 sentence pairs. We fine-tuned both the mT5 multilingual model and the NLLB200 model on this dataset. NLLB-200 outperformed mT5, achieving BLEU scores of 26.64 for English-Efik and 31.21 for Efik-English, with corresponding chrF scores of 51.04 and 47.92, indicating improved fluency and semantic fidelity. Our findings demonstrate the feasibility of developing practical machine translation tools for low-resource languages and highlight the importance of inclusive data practices and culturally grounded evaluation in advancing equitable NLP.
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。
