  Product attribute-value identification (PAVI) has been studied to link
products on e-commerce sites with their attribute values (e.g., <Material,
Cotton>) using product text as clues. Technical demands from real-world
e-commerce platforms require PAVI methods to handle unseen values,
multi-attribute values, and canonicalized values, which are only partly
addressed in existing extraction- and classification-based approaches.
Motivated by this, we explore a generative approach to the PAVI task. We
finetune a pre-trained generative model, T5, to decode a set of attribute-value
pairs as a target sequence from the given product text. Since the attribute
value pairs are unordered set elements, how to linearize them will matter; we,
thus, explore methods of composing an attribute-value pair and ordering the
pairs for the task. Experimental results confirm that our generation-based
approach outperforms the existing extraction and classification-based methods
on large-scale real-world datasets meant for those methods.
