Product Function Need Recognition via Semi-supervised Attention Network
This addresses the challenge for customers in assessing product functionality before purchase, though it is incremental as it builds on existing sequence labeling methods with semi-supervised learning.
The paper tackled the problem of identifying product functions from customer questions by introducing a novel QA corpus dense on functionality information and proposing a Semi-supervised Attention Network (SAN) for sequence labeling, achieving high coverage and accuracy compared to baselines.
Functionality is of utmost importance to customers when they purchase products. However, it is unclear to customers whether a product can really satisfy their needs on functions. Further, missing functions may be intentionally hidden by the manufacturers or the sellers. As a result, a customer needs to spend a fair amount of time before purchasing or just purchase the product on his/her own risk. In this paper, we first identify a novel QA corpus that is dense on product functionality information \footnote{The annotated corpus can be found at \url{https://www.cs.uic.edu/~hxu/}.}. We then design a neural network called Semi-supervised Attention Network (SAN) to discover product functions from questions. This model leverages unlabeled data as contextual information to perform semi-supervised sequence labeling. We conduct experiments to show that the extracted function have both high coverage and accuracy, compared with a wide spectrum of baselines.