Zero-Shot Multi-Label Topic Inference with Sentence Encoders

Souvika Sarkar; Dongji Feng; Shubhra Kanti Karmaker Santu

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arXiv · 2304.07382

Zero-Shot Multi-Label Topic Inference with Sentence Encoders

Souvika Sarkar·Dongji Feng·Shubhra Kanti Karmaker Santu

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Abstract

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.

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Souvika Sarkar, Dongji Feng, Shubhra Kanti Karmaker Santu. 2023-04-14. Zero-Shot Multi-Label Topic Inference with Sentence Encoders. https://arxiv.org/abs/2304.07382

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