  Multiclass classification (MCC) is a fundamental machine learning problem of
classifying each instance into one of a predefined set of classes. In the deep
learning era, extensive efforts have been spent on developing more powerful
neural embedding models to better represent the instance for improving MCC
performance. In this paper, we do not aim to propose new neural models for
instance representation learning, but to show that it is promising to boost MCC
performance with a novel formulation through the lens of ranking. In
particular, by viewing MCC as to rank classes for an instance, we first argue
that ranking metrics, such as Normalized Discounted Cumulative Gain, can be
more informative than the commonly used Top-$K$ metrics. We further demonstrate
that the dominant neural MCC recipe can be transformed to a neural ranking
framework. Based on such generalization, we show that it is intuitive to
leverage advanced techniques from the learning to rank literature to improve
the MCC performance out of the box. Extensive empirical results on both text
and image classification tasks with diverse datasets and backbone neural models
show the value of our proposed framework.
