发表机构
Leiden University Medical Center; LIACS, Leiden University（莱顿大学医学中心; 莱顿大学LIACS）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于翻译的微调在低资源语言NLP中的可行性，通过六个任务、五种语言数据集比较其与母语BERT性能，发现该方法对特定任务和语言有效，为扩展NLP到低资源语言提供可扩展、高效途径，推进语言包容性和可持续性。
AI 中文摘要
BERT模型通过处理跨领域非结构化文本的能力革新了自然语言处理（NLP）。然而，为非英语语言开发高质量BERT模型因注释数据有限和计算需求高而仍具挑战性。将非英语数据翻译成英语并微调现有英语BERT模型提供了一种资源高效的替代方案，但很少有研究在任务和语言上对基于翻译的微调与母语BERT性能进行结构比较。本研究进行了这样的比较，使用从保加利亚语、中文、荷兰语、意大利语和俄语翻译而来的数据集，评估了基于翻译的微调在六个NLP任务中的可行性。在所有设置中，基于翻译的方法在53.3%的情况下具有可比性或更优。在问答、词性标注和自然语言推理中收益最频繁，而在命名实体识别和仇恨言论检测中性能下降很常见。结果表明，基于翻译的微调对于依赖句法或结构模式的任务以及类型上与英语接近的语言（如荷兰语）最有效，但对于词元级或文化细微差别的任务效果较差，尤其是中文。总体而言，本研究表明基于翻译的微调为将NLP扩展到低资源语言提供了一条可扩展、资源高效且经过实证验证的途径，同时推进了人工智能中的语言包容性和可持续性。
英文摘要
BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains. However, developing high-quality BERT models for non-English languages remains challenging due to limited annotated data and high computational demands. Translating non-English data into English and fine-tuning existing English BERT models offers a resource-efficient alternative, yet few studies have structurally compared translation-based fine-tuning with native-language BERT performance across tasks and languages. This study provides such a comparison, evaluating the feasibility of translation-based fine-tuning across six NLP tasks: Sentiment Analysis, Hate Speech Detection, Question Answering, Named Entity Recognition, Part-of-Speech Tagging, and Natural Language Inference, using datasets translated from Bulgarian, Chinese, Dutch, Italian, and Russian. Across all settings, the translation-based approach was comparable or superior in 53.3 percent of cases. Gains were most frequent in Question Answering, Part-of-Speech Tagging, and Natural Language Inference, while performance declines were common in Named Entity Recognition and Hate Speech Detection. The results show that translation-based fine-tuning is most effective for tasks relying on syntactic or structural patterns and for languages typologically close to English, such as Dutch, but less effective for token-level or culturally nuanced tasks, particularly in Chinese. Overall, this study demonstrates that translation-based fine-tuning offers a scalable, resource-efficient, and empirically validated path for extending NLP to low-resource languages while advancing linguistic inclusivity and sustainability in artificial intelligence.
Comments26 pages, 1 figure
