@inproceedings{duong-etal-2018-active,
title = "Active learning for deep semantic parsing",
author = "Duong, Long and
Afshar, Hadi and
Estival, Dominique and
Pink, Glen and
Cohen, Philip and
Johnson, Mark",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2008",
doi = "10.18653/v1/P18-2008",
pages = "43--48",
abstract = "Semantic parsing requires training data that is expensive and slow to collect. We apply active learning to both traditional and {``}overnight{''} data collection approaches. We show that it is possible to obtain good training hyperparameters from seed data which is only a small fraction of the full dataset. We show that uncertainty sampling based on least confidence score is competitive in traditional data collection but not applicable for overnight collection. We propose several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.",
}
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<abstract>Semantic parsing requires training data that is expensive and slow to collect. We apply active learning to both traditional and “overnight” data collection approaches. We show that it is possible to obtain good training hyperparameters from seed data which is only a small fraction of the full dataset. We show that uncertainty sampling based on least confidence score is competitive in traditional data collection but not applicable for overnight collection. We propose several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.</abstract>
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%0 Conference Proceedings
%T Active learning for deep semantic parsing
%A Duong, Long
%A Afshar, Hadi
%A Estival, Dominique
%A Pink, Glen
%A Cohen, Philip
%A Johnson, Mark
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F duong-etal-2018-active
%X Semantic parsing requires training data that is expensive and slow to collect. We apply active learning to both traditional and “overnight” data collection approaches. We show that it is possible to obtain good training hyperparameters from seed data which is only a small fraction of the full dataset. We show that uncertainty sampling based on least confidence score is competitive in traditional data collection but not applicable for overnight collection. We propose several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.
%R 10.18653/v1/P18-2008
%U https://aclanthology.org/P18-2008
%U https://doi.org/10.18653/v1/P18-2008
%P 43-48
Markdown (Informal)
[Active learning for deep semantic parsing](https://aclanthology.org/P18-2008) (Duong et al., ACL 2018)
ACL
- Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen, and Mark Johnson. 2018. Active learning for deep semantic parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 43–48, Melbourne, Australia. Association for Computational Linguistics.