Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly focused on a symmetric problem setting where both input instances and label features are short-text in nature. Short-text XMC with label features has found numerous applications in areas such as query-to-ad-phrase matching in search ads, title-based product recommendation, prediction of related searches. In this paper, we propose Gandalf, a novel approach which makes use of a label co-occurrence graph to leverage label features as additional data points to supplement the training distribution. By exploiting the characteristics of the short-text XMC problem, it leverages the label features to construct valid training instances, and uses the label graph for generating the corresponding soft-label targets, hence effectively capturing the label-label correlations. Surprisingly, models trained on these new training instances, although being less than half of the original dataset, can outperform models trained on the original dataset, particularly on the PSP@k metric for tail labels. With this insight, we aim to train existing XMC algorithms on both, the original and new training instances, leading to an average 5% relative improvements for 6 state-of-the-art algorithms across 4 benchmark datasets consisting of up to 1.3M labels. Gandalf can be applied in a plug-and-play manner to various methods and thus forwards the state-of-the-art in the domain, without incurring any additional computational overheads.

翻译：极端多标签文本分类（XMC）涉及学习一个分类器，该分类器能够从数百万个标签候选中为输入分配一个最相关标签的子集。该领域近期的工作逐渐聚焦于一个对称问题设定，其中输入实例和标签特征本质上均为短文本。带有标签特征的短文本XMC已在搜索广告中的查询-广告短语匹配、基于标题的产品推荐以及相关搜索预测等领域找到了众多应用。在本文中，我们提出Gandalf，这是一种新颖方法，利用标签共现图将标签特征作为额外数据点来补充训练分布。通过利用短文本XMC问题的特性，该方法借助标签特征构建有效的训练实例，并使用标签图生成相应的软标签目标，从而有效捕捉标签-标签相关性。令人惊讶的是，在这些新训练实例上训练的模型（尽管其规模不到原始数据集的一半）在性能上可超越原始数据集训练的模型，尤其是在尾部标签的PSP@k指标上。基于这一洞察，我们旨在同时使用原始训练实例和新训练实例来训练现有XMC算法，从而在包含多达130万个标签的4个基准数据集上，使6种最先进算法的性能平均相对提升5%。Gandalf可以即插即用的方式应用于多种方法，从而推动该领域的最新进展，且不引入任何额外计算开销。