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
- Editors:
- Shervin Malmasi, Surya Kallumadi, Nicola Ueffing, Oleg Rokhlenko, Eugene Agichtein, Ido Guy
- Venue:
- ECNLP
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.ecnlp-1.18.pdf