Generating Rich Product Descriptions for Conversational E-commerce Systems
Shashank Kedia, Aditya Mantha, Sneha Gupta, Stephen Guo, Kannan Achan

TL;DR
This paper introduces eBERT, a sequence-to-sequence model that generates natural, spoken language product descriptions from webpage titles, improving voice-based e-commerce interactions.
Contribution
The paper presents eBERT, a novel pre-trained BERT-based model specifically designed for generating conversational product descriptions in e-commerce.
Findings
eBERT outperforms baseline models in experiments
Human evaluations favor eBERT-generated descriptions
Deployed successfully in real-world applications
Abstract
Through recent advancements in speech technologies and introduction of smart assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number of users are interacting with various applications through voice commands. E-commerce companies typically display short product titles on their webpages, either human-curated or algorithmically generated, when brevity is required. However, these titles are dissimilar from natural spoken language. For example, "Lucky Charms Gluten Free Break-fast Cereal, 20.5 oz a box Lucky Charms Gluten Free" is acceptable to display on a webpage, while a similar title cannot be used in a voice based text-to-speech application. In such conversational systems, an easy to comprehend sentence, such as "a 20.5 ounce box of lucky charms gluten free cereal" is preferred. Compared to display devices, where images and detailed product information can be…
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Taxonomy
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Dropout · Linear Warmup With Linear Decay · Layer Normalization · WordPiece · Dense Connections · Weight Decay · Refunds@Expedia|||How do I get a full refund from Expedia?
