Large-scale multi-label text classification (LMTC) aims to associate a
document with its relevant labels from a large candidate set. Most existing
LMTC approaches rely on massive human-annotated training data, which are often
costly to obtain and suffer from a long-tailed label distribution (i.e., many
labels occur only a few times in the training set). In this paper, we study
LMTC under the zero-shot setting, which does not require any annotated
documents with labels and only relies on label surface names and descriptions.
To train a classifier that calculates the similarity score between a document
and a label, we propose a novel metadata-induced contrastive learning (MICoL)
method. Different from previous text-based contrastive learning techniques,
MICoL exploits document metadata (e.g., authors, venues, and references of
research papers), which are widely available on the Web, to derive similar
document-document pairs. Experimental results on two large-scale datasets show
that: (1) MICoL significantly outperforms strong zero-shot text classification
and contrastive learning baselines; (2) MICoL is on par with the
state-of-the-art supervised metadata-aware LMTC method trained on 10K-200K
labeled documents; and (3) MICoL tends to predict more infrequent labels than
supervised methods, thus alleviates the deteriorated performance on long-tailed
labels.

本研究使用元数据引导对比学习的方法来训练文本分类模型，从而在没有标注文件的情况下，提供更好的多标签文本分类性能，特别是在训练数据呈现长尾标签分布时。

基于元数据诱导的对比学习，用于零样本多标签文本分类

Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification

Although fully generative models have been successfully used to model the
contents of text documents, they are often awkward to apply to combinations of
text data and document metadata. In this paper we propose a
Dirichlet-multinomial regression (DMR) topic model that includes a log-linear
prior on document-topic distributions that is a function of observed features
of the document, such as author, publication venue, references, and dates. We
show that by selecting appropriate features, DMR topic models can meet or
exceed the performance of several previously published topic models designed
for specific data.

本文提出一种基于 DMR 主题模型的 log-linear 先验方法，以元数据特征为参数，以模拟文档中词汇的分布和主题的分布。实验结果表明 DMR 主题模型可以超越以往的主题模型，从而提高了对文档元数据的建模能力。