MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification

The eXtreme Multi-label text Classification (XMC) refers to training a classifier that assigns a text sample with relevant labels from an extremely large-scale label set (e.g., millions of labels). We propose MatchXML, an efficient textlabel matching framework for XMC. We observe that the label embeddings generated from the sparse Term Frequency-Inverse Document Frequency (TF–IDF) features have several limitations. We thus propose label2vec to effectively train the semantic dense label embeddings by the Skip-gram model. The dense label embeddings are then used to build a Hierarchical Label Tree by clustering. In fine-tuning the pre-trained encoder Transformer, we formulate the multi-label text classification as a text-label matching problem in a bipartite graph. We then extract the dense text representations from the fine-tuned Transformer. Besides the fine-tuned dense text embeddings, we also extract the static dense sentence embeddings from a pre-trained Sentence Transformer. Finally, a linear ranker is trained by utilizing the sparse TF–IDF features, the fine-tuned dense text representations, and static dense sentence features. Experimental results demonstrate that MatchXML achieves the state-of-the-art accuracies on five out of six datasets. As for the training speed, MatchXML outperforms the competing methods on all the six datasets. Our source code is publicly available at
https://github.com/huiyegit/MatchXML
.