@inproceedings{heck-etal-2023-chatgpt,
title = "{C}hat{GPT} for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?",
author = "Heck, Michael and
Lubis, Nurul and
Ruppik, Benjamin and
Vukovic, Renato and
Feng, Shutong and
Geishauser, Christian and
Lin, Hsien-chin and
van Niekerk, Carel and
Gasic, Milica",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.81",
doi = "10.18653/v1/2023.acl-short.81",
pages = "936--950",
abstract = "Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. However, performance gains heavily depend on aggressive data augmentation and fine-tuning of ever larger language model based architectures. In contrast, general purpose language models, trained on large amounts of diverse data, hold the promise of solving any kind of task without task-specific training. We present preliminary experimental results on the ChatGPT research preview, showing that ChatGPT achieves state-of-the-art performance in zero-shot DST. Despite our findings, we argue that properties inherent to general purpose models limit their ability to replace specialized systems. We further theorize that the in-context learning capabilities of such models will likely become powerful tools to support the development of dedicated dialog state trackers and enable dynamic methods.",
}
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<abstract>Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. However, performance gains heavily depend on aggressive data augmentation and fine-tuning of ever larger language model based architectures. In contrast, general purpose language models, trained on large amounts of diverse data, hold the promise of solving any kind of task without task-specific training. We present preliminary experimental results on the ChatGPT research preview, showing that ChatGPT achieves state-of-the-art performance in zero-shot DST. Despite our findings, we argue that properties inherent to general purpose models limit their ability to replace specialized systems. We further theorize that the in-context learning capabilities of such models will likely become powerful tools to support the development of dedicated dialog state trackers and enable dynamic methods.</abstract>
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%0 Conference Proceedings
%T ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?
%A Heck, Michael
%A Lubis, Nurul
%A Ruppik, Benjamin
%A Vukovic, Renato
%A Feng, Shutong
%A Geishauser, Christian
%A Lin, Hsien-chin
%A van Niekerk, Carel
%A Gasic, Milica
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F heck-etal-2023-chatgpt
%X Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. However, performance gains heavily depend on aggressive data augmentation and fine-tuning of ever larger language model based architectures. In contrast, general purpose language models, trained on large amounts of diverse data, hold the promise of solving any kind of task without task-specific training. We present preliminary experimental results on the ChatGPT research preview, showing that ChatGPT achieves state-of-the-art performance in zero-shot DST. Despite our findings, we argue that properties inherent to general purpose models limit their ability to replace specialized systems. We further theorize that the in-context learning capabilities of such models will likely become powerful tools to support the development of dedicated dialog state trackers and enable dynamic methods.
%R 10.18653/v1/2023.acl-short.81
%U https://aclanthology.org/2023.acl-short.81
%U https://doi.org/10.18653/v1/2023.acl-short.81
%P 936-950
Markdown (Informal)
[ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?](https://aclanthology.org/2023.acl-short.81) (Heck et al., ACL 2023)
ACL
- Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, and Milica Gasic. 2023. ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 936–950, Toronto, Canada. Association for Computational Linguistics.