  Product attribute values are essential in many e-commerce scenarios, such as
customer service robots, product recommendations, and product retrieval. While
in the real world, the attribute values of a product are usually incomplete and
vary over time, which greatly hinders the practical applications. In this
paper, we propose a multimodal method to jointly predict product attributes and
extract values from textual product descriptions with the help of the product
images. We argue that product attributes and values are highly correlated,
e.g., it will be easier to extract the values on condition that the product
attributes are given. Thus, we jointly model the attribute prediction and value
extraction tasks from multiple aspects towards the interactions between
attributes and values. Moreover, product images have distinct effects on our
tasks for different product attributes and values. Thus, we selectively draw
useful visual information from product images to enhance our model. We annotate
a multimodal product attribute value dataset that contains 87,194 instances,
and the experimental results on this dataset demonstrate that explicitly
modeling the relationship between attributes and values facilitates our method
to establish the correspondence between them, and selectively utilizing visual
product information is necessary for the task. Our code and dataset will be
released to the public.
