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

TL;DR

This paper introduces Entity Transformer, a neural network model that accurately extracts multiple product names from voice ordering utterances, improving e-commerce voice systems' ability to handle complex orders.

Contribution

The paper presents a novel Entity Transformer architecture capable of recognizing up to 10 product entities in voice utterances, surpassing existing models like BERT in this task.

Findings

Entity Transformer achieves 12% performance improvement over non-neural models.

The model outperforms BERT in recognizing multiple product entities.

Improves shopping efficiency and user experience in voice-based e-commerce systems.

Abstract

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.