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 text-label 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
state-of-the-art accuracy on five out of six datasets. As for the speed,
MatchXML outperforms the competing methods on all the six datasets. Our source
code is publicly available at this https URL

通过提出 MatchXML 框架，我们在 eXtreme Multi-label text Classification 领域取得了 state-of-the-art 的准确率和速度，其中关键技术包括 label embeddings、Hierarchical Label Tree 和 linear ranker。