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.
@article{arxiv.2304.07382,
title = {Zero-Shot Multi-Label Topic Inference with Sentence Encoders},
author = {Souvika Sarkar and Dongji Feng and Shubhra Kanti Karmaker Santu},
journal= {arXiv preprint arXiv:2304.07382},
year = {2023}
}