低资源语言的跨语攻击性语言识别:以马拉地语为例

Saurabh Gaikwad, Tharindu Ranasinghe, Marcos Zampieri, C. Homan
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引用次数: 42

摘要

社交媒体上广泛存在的攻击性语言促使开发能够自动识别此类内容的系统。除了少数值得注意的例外,大多数关于攻击性语言自动识别的研究都是针对英语的。为了解决这个缺点,我们引入了MOLD,马拉地攻击性语言数据集。MOLD是第一个为马拉地语编写的数据集,从而为低资源的印度雅利安语言的研究开辟了一个新的领域。我们展示了在该数据集上进行的几个机器学习实验的结果,包括对孟加拉语、英语和印地语现有数据进行的最先进的跨语言转换器的零短和其他迁移学习实验。
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Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi
The widespread presence of offensive language on social media motivated the development of systems capable of recognizing such content automatically. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English. To address this shortcoming, we introduce MOLD, the Marathi Offensive Language Dataset. MOLD is the first dataset of its kind compiled for Marathi, thus opening a new domain for research in low-resource Indo-Aryan languages. We present results from several machine learning experiments on this dataset, including zero-short and other transfer learning experiments on state-of-the-art cross-lingual transformers from existing data in Bengali, English, and Hindi.
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