@inproceedings{hu-etal-2024-exploring,
title = "Exploring Description-Augmented Dataless Intent Classification",
author = "Hu, Ruoyu and
Khosmood, Foaad and
Edalat, Abbas",
editor = "Nouri, Elnaz and
Rastogi, Abhinav and
Spithourakis, Georgios and
Liu, Bing and
Chen, Yun-Nung and
Li, Yu and
Albalak, Alon and
Wakaki, Hiromi and
Papangelis, Alexandros",
booktitle = "Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nlp4convai-1.2",
pages = "13--36",
abstract = "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.",
}
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%0 Conference Proceedings
%T Exploring Description-Augmented Dataless Intent Classification
%A Hu, Ruoyu
%A Khosmood, Foaad
%A Edalat, Abbas
%Y Nouri, Elnaz
%Y Rastogi, Abhinav
%Y Spithourakis, Georgios
%Y Liu, Bing
%Y Chen, Yun-Nung
%Y Li, Yu
%Y Albalak, Alon
%Y Wakaki, Hiromi
%Y Papangelis, Alexandros
%S Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F hu-etal-2024-exploring
%X 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.
%U https://aclanthology.org/2024.nlp4convai-1.2
%P 13-36
Markdown (Informal)
[Exploring Description-Augmented Dataless Intent Classification](https://aclanthology.org/2024.nlp4convai-1.2) (Hu et al., NLP4ConvAI-WS 2024)
ACL