用真标签进行语言变体识别
Language Variety Identification with True Labels
摘要：
语言识别是许多IR和NLP应用中重要的第一步。然而，大多数公开可用的语言识别数据集都是在假设每个实例的黄金标签由文本的检索位置确定的情况下编译的。研究表明，这是一个有问题的假设，特别是在非常相似的语言的情况下（例如，克罗地亚语和塞尔维亚语）和民族语言变体（例如，巴西和欧洲葡萄牙语），其中文本可能不包含特定语言或变体的独特标记。为了克服这一重要限制，本文提出了DSL True Labels（DSL-TL），这是第一个用于语言种类识别的人工注释多语言数据集。DSL-TL总共包含12，900个葡萄牙语实例，分为欧洲葡萄牙语和巴西葡萄牙语;西班牙语，分为阿根廷西班牙语和卡斯蒂利亚西班牙语;英语，分为美国英语和英国英语。我们训练了多个模型来区分这些语言变体，并详细介绍了结果。本文提出的数据和模型提供了一个可靠的基准，朝着强大的和公平的语言品种识别系统的发展。我们向研究社区免费提供DSL-TL。
英文摘要：
Language identification is an important first step in many IR and NLP applications. Most publicly available language identification datasets, however, are compiled under the assumption that the gold label of each instance is determined by where texts are retrieved from. Research has shown that this is a problematic assumption, particularly in the case of very similar languages (e.g., Croatian and Serbian) and national language varieties (e.g., Brazilian and European Portuguese), where texts may contain no distinctive marker of the particular language or variety. To overcome this important limitation, this paper presents DSL True Labels (DSL-TL), the first human-annotated multilingual dataset for language variety identification. DSL-TL contains a total of 12,900 instances in Portuguese, split between European Portuguese and Brazilian Portuguese; Spanish, split between Argentine Spanish and Castilian Spanish; and English, split between American English and British English. We trained multiple models to discriminate between these language varieties, and we present the results in detail. The data and models presented in this paper provide a reliable benchmark toward the development of robust and fairer language variety identification systems. We make DSL-TL freely available to the research community.
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。
