Large Scale Taxonomy Classification using BiLSTM with Self-Attention

Abstract

In this paper we present a deep learning model for the task of large scale taxonomy classification, where the model is expected to predict the corresponding category ID path given a product title. The proposed approach relies on a Bidirectional Long Short Term Memory Network (BiLSTM) to capture the context information for each word, followed by a multi-head attention model to aggregate useful information from these words as the final representation of the product title. Our model adopts an end-to-end architecture that does not rely on any hand-craft features, and is regulated by various techniques.