@inproceedings{lin-etal-2023-selective,
title = "Selective In-Context Data Augmentation for Intent Detection using Pointwise {V}-Information",
author = "Lin, Yen-Ting and
Papangelis, Alexandros and
Kim, Seokhwan and
Lee, Sungjin and
Hazarika, Devamanyu and
Namazifar, Mahdi and
Jin, Di and
Liu, Yang and
Hakkani-Tur, Dilek",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.107",
doi = "10.18653/v1/2023.eacl-main.107",
pages = "1463--1476",
abstract = "This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28{\%} absolute improvement in 5-shot and 1.18{\%} absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01{\%} absolute, on average).",
}
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<abstract>This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28% absolute improvement in 5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01% absolute, on average).</abstract>
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%0 Conference Proceedings
%T Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
%A Lin, Yen-Ting
%A Papangelis, Alexandros
%A Kim, Seokhwan
%A Lee, Sungjin
%A Hazarika, Devamanyu
%A Namazifar, Mahdi
%A Jin, Di
%A Liu, Yang
%A Hakkani-Tur, Dilek
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F lin-etal-2023-selective
%X This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28% absolute improvement in 5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01% absolute, on average).
%R 10.18653/v1/2023.eacl-main.107
%U https://aclanthology.org/2023.eacl-main.107
%U https://doi.org/10.18653/v1/2023.eacl-main.107
%P 1463-1476
Markdown (Informal)
[Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information](https://aclanthology.org/2023.eacl-main.107) (Lin et al., EACL 2023)
ACL
- Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Sungjin Lee, Devamanyu Hazarika, Mahdi Namazifar, Di Jin, Yang Liu, and Dilek Hakkani-Tur. 2023. Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1463–1476, Dubrovnik, Croatia. Association for Computational Linguistics.