  Automatic annotation of short-text data to a large number of target labels,
referred to as Short Text Extreme Classification, has found numerous
applications including prediction of related searches and product
recommendation. In this paper, we propose a convolutional architecture
InceptionXML which is light-weight, yet powerful, and robust to the inherent
lack of word-order in short-text queries encountered in search and
recommendation. We demonstrate the efficacy of applying convolutions by
recasting the operation along the embedding dimension instead of the word
dimension as applied in conventional CNNs for text classification. Towards
scaling our model to datasets with millions of labels, we also propose SyncXML
pipeline which improves upon the shortcomings of the recently proposed dynamic
hard-negative mining technique for label short-listing by synchronizing the
label-shortlister and extreme classifier. SyncXML not only reduces the
inference time to half but is also an order of magnitude smaller than
state-of-the-art Astec in terms of model size. Through a comprehensive
empirical comparison, we show that not only can InceptionXML outperform
existing approaches on benchmark datasets but also the transformer baselines
requiring only 2% FLOPs. The code for InceptionXML is available at
https://github.com/xmc-aalto/inceptionxml.
