  Short text classification is a crucial and challenging aspect of Natural
Language Processing. For this reason, there are numerous highly specialized
short text classifiers. However, in recent short text research, State of the
Art (SOTA) methods for traditional text classification, particularly the pure
use of Transformers, have been unexploited. In this work, we examine the
performance of a variety of short text classifiers as well as the top
performing traditional text classifier. We further investigate the effects on
two new real-world short text datasets in an effort to address the issue of
becoming overly dependent on benchmark datasets with a limited number of
characteristics. Our experiments unambiguously demonstrate that Transformers
achieve SOTA accuracy on short text classification tasks, raising the question
of whether specialized short text techniques are necessary.
