  Sentence encoders have indeed been shown to achieve superior performances for
many downstream text-mining tasks and, thus, claimed to be fairly general.
Inspired by this, we performed a detailed study on how to leverage these
sentence encoders for the "zero-shot topic inference" task, where the topics
are defined/provided by the users in real-time. Extensive experiments on seven
different datasets demonstrate that Sentence-BERT demonstrates superior
generality compared to other encoders, while Universal Sentence Encoder can be
preferred when efficiency is a top priority.
