Exploring Description-Augmented Dataless Intent Classification

Autor:	Hu, Ruoyu, Khosmood, Foaad, Edalat, Abbas
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our methods on four commonly used intent classification datasets and compare against previous works of a similar nature. Our work shows promising results for dataless classification scaling to a large number of unseen intents. We show competitive results and significant improvements (+6.12\% Avg.) over strong zero-shot baselines, all without training on labelled or task-specific data. Furthermore, we provide qualitative error analysis of the shortfalls of this methodology to help guide future research in this area. Comment: Accepted to the 6th NLP for Conversational AI Workshop at ACL 2024(NLP4ConvAI)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.17862 Zobrazit plný text záznamu View this record from Arxiv