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.# 学习量化（Learning to Quantify） LeQua 2022（即CLEF首届“学习量化”评测实验室）的核心目标，是为文本数据集上的“学习量化”方法提供对比评估平台。所谓“学习量化”，指的是训练预测器以估算未标注文本文档集合中目标类别的相对频率。此类预测器被称为量化器（quantifier），需针对多个此类文档集合生成频率预测结果，其中部分集合的类别频率与训练集的类别频率存在显著差异。 ## 链接 https://lequa2022.github.io/ https://github.com/HLT-ISTI/LeQua2022_scripts ## 任务 T1A：本任务旨在评估二元量化器，即仅需预测单个类别及其补集相对频率的量化器。参与本任务的参与者将获得已转换为向量形式的文档，因此该任务适合不愿开展文本文档表征生成工作、而是专注于优化学习量化方法的参与者。 T1B：本任务旨在评估单标签多分类量化器，即针对每个文档恰好属于n>2个类别集合中某一类的场景进行量化的量化器。与任务T1A一致，参与本任务的参与者将获得已转换为向量形式的文档。 T2A：与任务T1A类似，本任务旨在评估二元量化器。但与任务T1A不同的是，参与本任务的参与者将获得文档的原始文本，因此该任务适合希望参与文本文档表征生成或训练端到端系统的参与者。 T2B：与任务T1B类似，本任务旨在评估单标签多分类量化器；与任务T2A一致，参与本任务的参与者将获得文档的原始文本。