InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification

TL;DR

This paper introduces InceptionXML, a lightweight convolutional framework with synchronized negative sampling for short text extreme classification, achieving high accuracy with reduced inference time and model size.

Contribution

The paper proposes InceptionXML, a novel convolutional architecture optimized for short text classification, and SyncXML, a synchronized negative sampling method that enhances scalability and efficiency.

Findings

InceptionXML outperforms existing methods on benchmark datasets.

SyncXML reduces inference time by half and model size by an order of magnitude.

InceptionXML requires only 2% FLOPs compared to transformer baselines.

Abstract

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.