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

A Sequence to Sequence Model for Extracting Multiple Product Name Entities from Dialog