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.

When a NLU model is updated, new utter- ances must be annotated to be included for training. However, manual annotation is very costly. We evaluate a semi-supervised learning workflow with a human in the loop in a produc- tion environment. The previous NLU model predicts the annotation of the new utterances, a human then reviews the predicted annotation. Only when the NLU prediction is assessed as incorrect the utterance is sent for human anno- tation. Experimental results show that the pro- posed workflow boosts the performance of the NLU model while significantly reducing the annotation volume. Specifically, in our setup, we see improvements of up to 14.16% for a recall-based metric and up to 9.57% for a F1- score based metric, while reducing the annota- tion volume by 97% and overall cost by 60% for each iteration.