  Price Per Unit (PPU) is an essential information for consumers shopping on
e-commerce websites when comparing products. Finding total quantity in a
product is required for computing PPU, which is not always provided by the
sellers. To predict total quantity, all relevant quantities given in a product
attributes such as title, description and image need to be inferred correctly.
We formulate this problem as a question-answering (QA) task rather than named
entity recognition (NER) task for fact extraction. In our QA approach, we first
predict the unit of measure (UoM) type (e.g., volume, weight or count), that
formulates the desired question (e.g., "What is the total volume?") and then
use this question to find all the relevant answers. Our model architecture
consists of two subnetworks for the two subtasks: a classifier to predict UoM
type (or the question) and an extractor to extract the relevant quantities. We
use a deep character-level CNN architecture for both subtasks, which enables
(1) easy expansion to new stores with similar alphabets, (2) multi-span
answering due to its span-image architecture and (3) easy deployment by keeping
model-inference latency low. Our QA approach outperforms rule-based methods by
34.4% in precision and also BERT-based fact extraction approach in all stores
globally, with largest precision lift of 10.6% in the US store.
