@inproceedings{krause-etal-2023-leveraging,
title = "Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation",
author = "Krause, Lea and
B{\'a}ez Santamar{\'\i}a, Selene and
van der Meer, Michiel and
Khurana, Urja",
editor = "Chen, Yun-Nung and
Crook, Paul and
Galley, Michel and
Ghazarian, Sarik and
Gunasekara, Chulaka and
Gupta, Raghav and
Hedayatnia, Behnam and
Kottur, Satwik and
Moon, Seungwhan and
Zhang, Chen",
booktitle = "Proceedings of The Eleventh Dialog System Technology Challenge",
month = sep,
year = "2023",
address = "Prague, Czech Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.dstc-1.22",
pages = "193--205",
abstract = "This paper discusses our approaches for task-oriented conversational modelling using subjective knowledge, with a particular emphasis on response generation. Our methodology was shaped by an extensive data analysis that evaluated key factors such as response length, sentiment, and dialogue acts present in the provided dataset. We used few-shot learning to augment the data with newly generated subjective knowledge items and present three approaches for DSTC11: (1) task-specific model exploration, (2) incorporation of the most frequent question into all generated responses, and (3) a waterfall prompting technique using a combination of both GPT-3 and ChatGPT.",
}
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%0 Conference Proceedings
%T Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation
%A Krause, Lea
%A Báez Santamaría, Selene
%A van der Meer, Michiel
%A Khurana, Urja
%Y Chen, Yun-Nung
%Y Crook, Paul
%Y Galley, Michel
%Y Ghazarian, Sarik
%Y Gunasekara, Chulaka
%Y Gupta, Raghav
%Y Hedayatnia, Behnam
%Y Kottur, Satwik
%Y Moon, Seungwhan
%Y Zhang, Chen
%S Proceedings of The Eleventh Dialog System Technology Challenge
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czech Republic
%F krause-etal-2023-leveraging
%X This paper discusses our approaches for task-oriented conversational modelling using subjective knowledge, with a particular emphasis on response generation. Our methodology was shaped by an extensive data analysis that evaluated key factors such as response length, sentiment, and dialogue acts present in the provided dataset. We used few-shot learning to augment the data with newly generated subjective knowledge items and present three approaches for DSTC11: (1) task-specific model exploration, (2) incorporation of the most frequent question into all generated responses, and (3) a waterfall prompting technique using a combination of both GPT-3 and ChatGPT.
%U https://aclanthology.org/2023.dstc-1.22
%P 193-205
Markdown (Informal)
[Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation](https://aclanthology.org/2023.dstc-1.22) (Krause et al., DSTC-WS 2023)
ACL