  The wide applicability of pretrained transformer models (PTMs) for natural
language tasks is well demonstrated, but their ability to comprehend short
phrases of text is less explored. To this end, we evaluate different PTMs from
the lens of unsupervised Entity Linking in task-oriented dialog across 5
characteristics -- syntactic, semantic, short-forms, numeric and phonetic. Our
results demonstrate that several of the PTMs produce sub-par results when
compared to traditional techniques, albeit competitive to other neural
baselines. We find that some of their shortcomings can be addressed by using
PTMs fine-tuned for text-similarity tasks, which illustrate an improved ability
in comprehending semantic and syntactic correspondences, as well as some
improvements for short-forms, numeric and phonetic variations in entity
mentions. We perform qualitative analysis to understand nuances in their
predictions and discuss scope for further improvements. Code can be found at
https://github.com/murali1996/el_tod
