Taxi1500: A Multilingual Dataset for Text Classification in 1500
  Languages
Chunlan Ma Ayyoob ImaniGooghari Haotian Ye Ehsaneddin Asgari Hinrich Schütze
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.