  Automatic extraction of product attributes from their textual descriptions is
essential for online shopper experience. One inherent challenge of this task is
the emerging nature of e-commerce products -- we see new types of products with
their unique set of new attributes constantly. Most prior works on this matter
mine new values for a set of known attributes but cannot handle new attributes
that arose from constantly changing data. In this work, we study the attribute
mining problem in an open-world setting to extract novel attributes and their
values. Instead of providing comprehensive training data, the user only needs
to provide a few examples for a few known attribute types as weak supervision.
We propose a principled framework that first generates attribute value
candidates and then groups them into clusters of attributes. The candidate
generation step probes a pre-trained language model to extract phrases from
product titles. Then, an attribute-aware fine-tuning method optimizes a
multitask objective and shapes the language model representation to be
attribute-discriminative. Finally, we discover new attributes and values
through the self-ensemble of our framework, which handles the open-world
challenge. We run extensive experiments on a large distantly annotated
development set and a gold standard human-annotated test set that we collected.
Our model significantly outperforms strong baselines and can generalize to
unseen attributes and product types.
