Taxi1500: A Multilingual Dataset for Text Classification in 1500 Languages
This addresses the problem of neglected languages in NLP for researchers and practitioners, though it is incremental as it builds on existing translation-based methods.
The authors tackled the lack of evaluation datasets for many languages by creating a text classification dataset covering over 1500 languages, including low-resource ones, using parallel Bible translations and label projection, and they benchmarked existing multilingual models on it.
While natural language processing tools have been developed extensively for some of the world's languages, a significant portion of the world's over 7000 languages are still neglected. One reason for this is that evaluation datasets do not yet cover a wide range of languages, including low-resource and endangered ones. We aim to address this issue by creating a text classification dataset encompassing a large number of languages, many of which currently have little to no annotated data available. We leverage parallel translations of the Bible to construct such a dataset by first developing applicable topics and employing a crowdsourcing tool to collect annotated data. By annotating the English side of the data and projecting the labels onto other languages through aligned verses, we generate text classification datasets for more than 1500 languages. We extensively benchmark several existing multilingual language models using our dataset. To facilitate the advancement of research in this area, we will release our dataset and code.