  E-commerce voice ordering systems need to recognize multiple product name
entities from ordering utterances. Existing voice ordering systems such as
Amazon Alexa can capture only a single product name entity. This restrains
users from ordering multiple items with one utterance. In recent years,
pre-trained language models, e.g., BERT and GPT-2, have shown promising results
on NLP benchmarks like Super-GLUE. However, they can't perfectly generalize to
this Multiple Product Name Entity Recognition (MPNER) task due to the ambiguity
in voice ordering utterances. To fill this research gap, we propose Entity
Transformer (ET) neural network architectures which recognize up to 10 items in
an utterance. In our evaluation, the best ET model (conveRT + ngram + ET) has a
performance improvement of 12% on our test set compared to the non-neural
model, and outperforms BERT with ET as well. This helps customers finalize
their shopping cart via voice dialog, which improves shopping efficiency and
experience.
