  Scalability and accuracy are well recognized challenges in deep extreme
multi-label learning where the objective is to train architectures for
automatically annotating a data point with the most relevant subset of labels
from an extremely large label set. This paper develops the DeepXML framework
that addresses these challenges by decomposing the deep extreme multi-label
task into four simpler sub-tasks each of which can be trained accurately and
efficiently. Choosing different components for the four sub-tasks allows
DeepXML to generate a family of algorithms with varying trade-offs between
accuracy and scalability. In particular, DeepXML yields the Astec algorithm
that could be 2-12% more accurate and 5-30x faster to train than leading deep
extreme classifiers on publically available short text datasets. Astec could
also efficiently train on Bing short text datasets containing up to 62 million
labels while making predictions for billions of users and data points per day
on commodity hardware. This allowed Astec to be deployed on the Bing search
engine for a number of short text applications ranging from matching user
queries to advertiser bid phrases to showing personalized ads where it yielded
significant gains in click-through-rates, coverage, revenue and other online
metrics over state-of-the-art techniques currently in production. DeepXML's
code is available at https://github.com/Extreme-classification/deepxml
