Exploring Description-Augmented Dataless Intent Classification

Fuente: arXiv
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Hauptverfasser: Hu, Ruoyu, Khosmood, Foaad, Edalat, Abbas
Format: Preprint
Veröffentlicht: 2024
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author Hu, Ruoyu
Khosmood, Foaad
Edalat, Abbas
author_facet Hu, Ruoyu
Khosmood, Foaad
Edalat, Abbas
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2407_17862
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Exploring Description-Augmented Dataless Intent Classification
Hu, Ruoyu
Khosmood, Foaad
Edalat, Abbas
Computation and Language
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.
title Exploring Description-Augmented Dataless Intent Classification
topic Computation and Language
url https://arxiv.org/abs/2407.17862