Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
This addresses the challenge of classifying documents with many labels, particularly for tail labels with limited training data, offering an incremental improvement in XMTC.
The paper tackles extreme multi-label text classification (XMTC) by proposing a hybrid attention deep neural network model (LAHA) that creates label-aware document representations, achieving superior performance especially on tail labels compared to state-of-the-art methods on six benchmark datasets.
Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially for the tail labels because there are only few training documents to build classifier. This paper is motivated to better explore the semantic relationship between each document and extreme labels by taking advantage of both document content and label correlation. Our objective is to establish an explicit label-aware representation for each document with a hybrid attention deep neural network model(LAHA). LAHA consists of three parts. The first part adopts a multi-label self-attention mechanism to detect the contribution of each word to labels. The second part exploits the label structure and document content to determine the semantic connection between words and labels in a same latent space. An adaptive fusion strategy is designed in the third part to obtain the final label-aware document representation so that the essence of previous two parts can be sufficiently integrated. Extensive experiments have been conducted on six benchmark datasets by comparing with the state-of-the-art methods. The results show the superiority of our proposed LAHA method, especially on the tail labels.