Language Variety Identification with True Labels
This addresses the need for fairer and more robust language variety identification in IR and NLP applications, though it is incremental as it focuses on dataset creation.
The paper tackles the problem of inaccurate gold labels in language identification datasets by introducing DSL True Labels (DSL-TL), a human-annotated dataset with 12,900 instances for distinguishing language varieties like Brazilian vs. European Portuguese, resulting in a reliable benchmark for training models.
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.