BGNN-XML: Bilateral Graph Neural Networks for Extreme Multi-label Text Classification
Daoming Zong, Shiliang Sun
Abstract
Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent attention due to massive label sets yielded by modern applications, such as news annotation and product recommendation. The main challenges of XMTC are the data <i>scalability</i> and <i>sparsity</i>, thereby leading to two issues: i) the intractability to scale to the extreme label setting, ii) the presence of long-tailed label distribution, implying that a large fraction of labels have few positive training instances. To overcome these problems, we propose BGNN-XML, a scalable graph neural network framework tailored for XMTC problems. Specifically, we exploit label correlations via excavating their co-occurrence patterns and build a label graph based on the correlation matrix. We then conduct the attributed graph clustering by performing graph convolution with a low-pass graph filter to jointly model label dependencies and label features, which induces semantic label clusters. We further propose a bilateral-branch graph isomorphism network to decouple representation learning and classifier learning for better modeling <i>tail labels</i>. Experimental results on multiple benchmark datasets demonstrate that BGNN-XML significantly outperforms state-of-the-art baselines while maintaining comparable prediction efficiency and model size.