Combining semantic search and twin product classification for recognition of purchasable items in voice shopping

Abstract

The accuracy of an online shopping system via voice commands is particularly important and may have a great impact on customer trust. This paper focuses on the problem of detecting if an utterance contains actual and purchasable products, thus referring to a shopping-related intent in a typical Spoken Language Understanding architecture consisting of an intent classifier and a slot detector. Searching through billions of products to check if a detected slot is a purchasable item is prohibitively expensive. To overcome this problem, we present a framework that (1) uses a retrieval module that returns the most relevant products with respect to the detected slot, and (2) combines it with a twin network that decides if the detected slot is indeed a purchasable item or not. Through various experiments, we show that this architecture outperforms a typical slot detector approach, with a gain of +81% in accuracy and +41% in F1 score.