@inproceedings{kulkarni-etal-2024-synthdst,
title = "{S}ynth{DST}: Synthetic Data is All You Need for Few-Shot Dialog State Tracking",
author = "Kulkarni, Atharva and
Tseng, Bo-Hsiang and
Moniz, Joel and
Piraviperumal, Dhivya and
Yu, Hong and
Bhargava, Shruti",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.120",
pages = "1988--2001",
abstract = "In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, expensive, and, at times, infeasible. While zero-shot learning requires no training data, it significantly lags behind the few-shot setup. Thus, {`}\textit{Can we efficiently generate synthetic data for any dialogue schema to enable few-shot prompting?}' Addressing this question, we propose , a data generation framework tailored for DST, utilizing LLMs. Our approach only requires the dialogue schema and a few hand-crafted dialogue templates to synthesize natural, coherent, and free-flowing dialogues with DST annotations. Few-shot learning using data from results in $4-5\%$ improvement in Joint Goal Accuracy over the zero-shot baseline on MultiWOZ 2.1 and 2.4. Remarkably, our few-shot learning approach recovers nearly 98{\%} of the performance compared to the few-shot setup using human-annotated training data.",
}
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<abstract>In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, expensive, and, at times, infeasible. While zero-shot learning requires no training data, it significantly lags behind the few-shot setup. Thus, ‘Can we efficiently generate synthetic data for any dialogue schema to enable few-shot prompting?’ Addressing this question, we propose , a data generation framework tailored for DST, utilizing LLMs. Our approach only requires the dialogue schema and a few hand-crafted dialogue templates to synthesize natural, coherent, and free-flowing dialogues with DST annotations. Few-shot learning using data from results in 4-5% improvement in Joint Goal Accuracy over the zero-shot baseline on MultiWOZ 2.1 and 2.4. Remarkably, our few-shot learning approach recovers nearly 98% of the performance compared to the few-shot setup using human-annotated training data.</abstract>
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%0 Conference Proceedings
%T SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
%A Kulkarni, Atharva
%A Tseng, Bo-Hsiang
%A Moniz, Joel
%A Piraviperumal, Dhivya
%A Yu, Hong
%A Bhargava, Shruti
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F kulkarni-etal-2024-synthdst
%X In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, expensive, and, at times, infeasible. While zero-shot learning requires no training data, it significantly lags behind the few-shot setup. Thus, ‘Can we efficiently generate synthetic data for any dialogue schema to enable few-shot prompting?’ Addressing this question, we propose , a data generation framework tailored for DST, utilizing LLMs. Our approach only requires the dialogue schema and a few hand-crafted dialogue templates to synthesize natural, coherent, and free-flowing dialogues with DST annotations. Few-shot learning using data from results in 4-5% improvement in Joint Goal Accuracy over the zero-shot baseline on MultiWOZ 2.1 and 2.4. Remarkably, our few-shot learning approach recovers nearly 98% of the performance compared to the few-shot setup using human-annotated training data.
%U https://aclanthology.org/2024.eacl-long.120
%P 1988-2001
Markdown (Informal)
[SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking](https://aclanthology.org/2024.eacl-long.120) (Kulkarni et al., EACL 2024)
ACL
- Atharva Kulkarni, Bo-Hsiang Tseng, Joel Moniz, Dhivya Piraviperumal, Hong Yu, and Shruti Bhargava. 2024. SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1988–2001, St. Julian’s, Malta. Association for Computational Linguistics.