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

Dieu-Thu Le, Verena Weber, Melanie Bradford


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 consist- ing of an intent classifier and a slot detec- tor. 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 rele- vant products with respect to the detected slot, and (2) combines it with a twin network that decides if the detected slot is indeed a pur- chasable item or not. Through various exper- iments, we show that this architecture outper- forms a typical slot detector approach, with a gain of +81% in accuracy and +41% in F1 score.
Anthology ID:
2021.ecnlp-1.18
Volume:
Proceedings of the 4th Workshop on e-Commerce and NLP
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | ECNLP | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
150–157
Language:
URL:
https://aclanthology.org/2021.ecnlp-1.18
DOI:
10.18653/v1/2021.ecnlp-1.18
Bibkey:
Cite (ACL):
Dieu-Thu Le, Verena Weber, and Melanie Bradford. 2021. Combining semantic search and twin product classification for recognition of purchasable items in voice shopping. In Proceedings of the 4th Workshop on e-Commerce and NLP, pages 150–157, Online. Association for Computational Linguistics.
Cite (Informal):
Combining semantic search and twin product classification for recognition of purchasable items in voice shopping (Le et al., ECNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.ecnlp-1.18.pdf