  Product attribute value extraction is an important task in e-Commerce which
can help several downstream applications such as product search and
recommendation. Most previous models handle this task using sequence labeling
or question answering method which rely on the sequential position information
of values in the product text and are vulnerable to data discrepancy between
training and testing. This limits their generalization ability to real-world
scenario in which each product can have multiple descriptions across various
shopping platforms with different composition of text and style. They also have
limited zero-shot ability to new values. In this paper, we propose a multi-task
learning model with value generation/classification and attribute prediction
called JPAVE to predict values without the necessity of position information of
values in the text. Furthermore, the copy mechanism in value generator and the
value attention module in value classifier help our model address the data
discrepancy issue by only focusing on the relevant part of input text and
ignoring other information which causes the discrepancy issue such as sentence
structure in the text. Besides, two variants of our model are designed for
open-world and closed-world scenarios. In addition, copy mechanism introduced
in the first variant based on value generation can improve its zero-shot
ability for identifying unseen values. Experimental results on a public dataset
demonstrate the superiority of our model compared with strong baselines and its
generalization ability of predicting new values.
