HAXMLNet: Hierarchical Attention Network for Extreme Multi-Label Text Classification
This addresses the problem of tagging text with relevant labels from an extreme-scale set for applications like document categorization, but it is incremental as it builds on existing attention-based methods.
The paper tackled extreme multi-label text classification by proposing HAXMLNet, a hierarchical attention network, and achieved competitive performance with state-of-the-art methods.
Extreme multi-label text classification (XMTC) addresses the problem of tagging each text with the most relevant labels from an extreme-scale label set. Traditional methods use bag-of-words (BOW) representations without context information as their features. The state-ot-the-art deep learning-based method, AttentionXML, which uses a recurrent neural network (RNN) and the multi-label attention, can hardly deal with extreme-scale (hundreds of thousands labels) problem. To address this, we propose our HAXMLNet, which uses an efficient and effective hierarchical structure with the multi-label attention. Experimental results show that HAXMLNet reaches a competitive performance with other state-of-the-art methods.