@inproceedings{le-etal-2021-combining,
    title = "Combining semantic search and twin product classification for recognition of purchasable items in voice shopping",
    author = "Le, Dieu-Thu  and
      Weber, Verena  and
      Bradford, Melanie",
    editor = "Malmasi, Shervin  and
      Kallumadi, Surya  and
      Ueffing, Nicola  and
      Rokhlenko, Oleg  and
      Agichtein, Eugene  and
      Guy, Ido",
    booktitle = "Proceedings of the 4th Workshop on e-Commerce and NLP",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.ecnlp-1.18/",
    doi = "10.18653/v1/2021.ecnlp-1.18",
    pages = "150--157",
    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."
}