%0 Conference Proceedings %T Combining semantic search and twin product classification for recognition of purchasable items in voice shopping %A Le, Dieu-Thu %A Weber, Verena %A Bradford, Melanie %Y Malmasi, Shervin %Y Kallumadi, Surya %Y Ueffing, Nicola %Y Rokhlenko, Oleg %Y Agichtein, Eugene %Y Guy, Ido %S Proceedings of the 4th Workshop on e-Commerce and NLP %D 2021 %8 August %I Association for Computational Linguistics %C Online %F le-etal-2021-combining %X 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. %R 10.18653/v1/2021.ecnlp-1.18 %U https://aclanthology.org/2021.ecnlp-1.18 %U https://doi.org/10.18653/v1/2021.ecnlp-1.18 %P 150-157