  Automatic extraction of product attribute values is an important enabling
technology in e-Commerce platforms. This task is usually modeled using sequence
labeling architectures, with several extensions to handle multi-attribute
extraction. One line of previous work constructs attribute-specific models,
through separate decoders or entirely separate models. However, this approach
constrains knowledge sharing across different attributes. Other contributions
use a single multi-attribute model, with different techniques to embed
attribute information. But sharing the entire network parameters across all
attributes can limit the model's capacity to capture attribute-specific
characteristics. In this paper we present AdaTag, which uses adaptive decoding
to handle extraction. We parameterize the decoder with pretrained attribute
embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This
allows for separate, but semantically correlated, decoders to be generated on
the fly for different attributes. This approach facilitates knowledge sharing,
while maintaining the specificity of each attribute. Our experiments on a
real-world e-Commerce dataset show marked improvements over previous methods.
