@inproceedings{heck-etal-2020-task,
title = "Out-of-Task Training for Dialog State Tracking Models",
author = "Heck, Michael and
Geishauser, Christian and
Lin, Hsien-chin and
Lubis, Nurul and
Moresi, Marco and
van Niekerk, Carel and
Gasic, Milica",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.596",
doi = "10.18653/v1/2020.coling-main.596",
pages = "6767--6774",
abstract = "Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications. In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers. This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.",
}
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<abstract>Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications. In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers. This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.</abstract>
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%0 Conference Proceedings
%T Out-of-Task Training for Dialog State Tracking Models
%A Heck, Michael
%A Geishauser, Christian
%A Lin, Hsien-chin
%A Lubis, Nurul
%A Moresi, Marco
%A van Niekerk, Carel
%A Gasic, Milica
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F heck-etal-2020-task
%X Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications. In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers. This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.
%R 10.18653/v1/2020.coling-main.596
%U https://aclanthology.org/2020.coling-main.596
%U https://doi.org/10.18653/v1/2020.coling-main.596
%P 6767-6774
Markdown (Informal)
[Out-of-Task Training for Dialog State Tracking Models](https://aclanthology.org/2020.coling-main.596) (Heck et al., COLING 2020)
ACL
- Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Carel van Niekerk, and Milica Gasic. 2020. Out-of-Task Training for Dialog State Tracking Models. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6767–6774, Barcelona, Spain (Online). International Committee on Computational Linguistics.