印尼语SNLI:印尼语的识别文本包含数据集,来源于斯坦福自然语言推理数据集
I Made Suwija Putra1,2, Daniel Siahaan1, Ahmad Saikhu1
1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Data in brief
|January 18, 2024
概括
我们介绍SNLI Indo,这是一个大规模的数据集,用于识别印尼语的文本包含 (RTE). 本资源支持开发印尼语NLP任务的高级深度学习模型.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
背景情况:
- 识别文本包含 (RTE) 对NLP至关重要,确定文本对之间的推理关系.
- 神经网络,特别是深度学习,是RTE的最新技术,但需要大量高质量的数据集.
研究的目的:
- 通过创建一个大规模的RTE数据集来解决印尼NLP的资源缺口.
- 促进印尼语RTE最佳深度学习模型的开发.
主要方法:
- 将英语斯坦福自然语言推理 (SNLI) 语料库翻译成印尼语.
- 使用谷歌云翻译API进行句子对翻译.
- 创建了SNLI Indo数据集,其中包含超过569,000个前提假设对.
主要成果:
- 成功开发了SNLI Indo,这是印度尼西亚RTE的大量数据集.
- 该数据集包括549,365次培训,9,840次验证和9,822次测试句子对.
- 在印度尼西亚,SNLI Indo弥合了印尼NLP资源的差距.
结论:
- 在印度尼西亚,SNLI Indo能够更有效地培训RTE的深度学习模型.
- 这一数据集对于推进印尼语言的NLP研究和应用至关重要.
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