Learning to quantify: LeQua 2022 datasets

# Learning to Quantify The aim of LeQua 2022 (the 1st edition of the CLEF “Learning to Quantify” lab) is to allow the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. These predictors (called “quantifiers”) will be required to issue predictions for several such sets, some of them characterized by class frequencies radically different from the ones of the training set. ## Links https://lequa2022.github.io/ https://github.com/HLT-ISTI/LeQua2022_scripts ## Tasks T1A: This task is concerned with evaluating binary quantifiers, i.e., quantifiers that must only predict the relative frequencies of a class and its complement. Participants in this task will be provided with documents already converted into vector form; the task is thus suitable for participants who do not wish to engage in generating representations for the textual documents, but want instead to concentrate on optimizing the methods for learning to quantify. T1B: This task is concerned with evaluating single-label multi-class quantifiers, i.e., quantifiers that operate on documents that each belong to exactly one among a set of n>2 classes. Like in Task T1A, participants will be provided with documents already converted in vector form. T2A: Like Task T1A, this task is concerned with evaluating binary quantifiers. Unlike in Task T1A, participants will be provided with the raw text of the documents; the task is thus suitable for participants who also wish to engage in generating suitable representations for the textual documents, or to train end-to-end systems. T2B: Like Task T1B, this task is concerned with evaluating single-label multi-class quantifiers; like in Task T2A, participants will be provided with the raw text of the documents.