Combining semantic search and twin product classification for recognition of purchasable items in voice shopping
Dieu-Thu Le, Verena Weber, Melanie Bradford · 2021
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.