Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context

Arpit Sharma


Abstract
In this work, we improve the intent classification in an English based e-commerce voice assistant by using inter-utterance context. For increased user adaptation and hence being more profitable, an e-commerce voice assistant is desired to understand the context of a conversation and not have the users repeat it in every utterance. For example, let a user’s first utterance be ‘find apples’. Then, the user may say ‘i want organic only’ to filter out the results generated by an assistant with respect to the first query. So, it is important for the assistant to take into account the context from the user’s first utterance to understand her intention in the second one. In this paper, we present our approach for contextual intent classification in Walmart’s e-commerce voice assistant. It uses the intent of the previous user utterance to predict the intent of her current utterance. With the help of experiments performed on real user queries we show that our approach improves the intent classification in the assistant.
Anthology ID:
2020.ecnlp-1.6
Volume:
Proceedings of The 3rd Workshop on e-Commerce and NLP
Month:
July
Year:
2020
Address:
Seattle, WA, USA
Venues:
ACL | ECNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
40–45
Language:
URL:
https://www.aclweb.org/anthology/2020.ecnlp-1.6
DOI:
10.18653/v1/2020.ecnlp-1.6
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ecnlp-1.6.pdf
Video:
 http://slideslive.com/38931248