Publication

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

By Dieu-Thu Le, Verena Weber, Melanie Bradford

2021

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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.

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