Long-tailed Extreme Multi-label Text Classification with Generated Pseudo Label Descriptions
This addresses the challenge of data scarcity for rare labels in large-scale classification, which is incremental as it combines existing methods in a novel way.
The paper tackles the problem of predicting rare labels in extreme multi-label text classification by generating pseudo label descriptions with a bag-of-words classifier and using neural retrieval models, achieving state-of-the-art performance on benchmark datasets and significant improvements in tail label prediction.
Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe data scarce problem associated with the long tail of rare labels in highly skewed distributions. This paper addresses the challenge of tail label prediction by proposing a novel approach, which combines the effectiveness of a trained bag-of-words (BoW) classifier in generating informative label descriptions under severe data scarce conditions, and the power of neural embedding based retrieval models in mapping input documents (as queries) to relevant label descriptions. The proposed approach achieves state-of-the-art performance on XMTC benchmark datasets and significantly outperforms the best methods so far in the tail label prediction. We also provide a theoretical analysis for relating the BoW and neural models w.r.t. performance lower bound.