Active learning for deep semantic parsing

Long Duong; Hadi Afshar; Dominique Estival; Glen Pink; Philip Cohen; Mark Johnson

doi:10.18653/v1/p18-2008

Active learning for deep semantic parsing

Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen, Mark Johnson

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

19 Citations (Scopus)

29 Downloads (Pure)

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 evaluate several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.

Original language	English
Title of host publication	ACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publication	Proceedings of the Conference, Vol. 2 (Short Papers)
Editors	Iryna Gurevych, Yusuke Miyao
Publisher	Association for Computational Linguistics (ACL)
Pages	43-48
Number of pages	6
Volume	2
ISBN (Electronic)	9781948087346
DOIs	https://doi.org/10.18653/v1/p18-2008
Publication status	Published - 1 Jan 2018
Externally published	Yes
Event	56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018

Conference

Conference	56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
Country/Territory	Australia
City	Melbourne
Period	15/07/18 → 20/07/18

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Access to Document

10.18653/v1/p18-2008

Publisher version (open access)Final published version, 338 KBLicence: CC BY

Cite this

Duong, L., Afshar, H., Estival, D., Pink, G., Cohen, P., & Johnson, M. (2018). Active learning for deep semantic parsing. In I. Gurevych, & Y. Miyao (Eds.), ACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers) (Vol. 2, pp. 43-48). [P18-2008] Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-2008

@inproceedings{d6143b5c36984851a249b12865f8ccd3,

title = "Active learning for deep semantic parsing",

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 evaluate several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.",

author = "Long Duong and Hadi Afshar and Dominique Estival and Glen Pink and Philip Cohen and Mark Johnson",

note = "Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.; 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 ; Conference date: 15-07-2018 Through 20-07-2018",

year = "2018",

month = jan,

day = "1",

doi = "10.18653/v1/p18-2008",

language = "English",

volume = "2",

pages = "43--48",

editor = "Iryna Gurevych and Yusuke Miyao",

booktitle = "ACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics (ACL)",

}

Duong, L, Afshar, H, Estival, D, Pink, G, Cohen, P & Johnson, M 2018, Active learning for deep semantic parsing. in I Gurevych & Y Miyao (eds), ACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers). vol. 2, P18-2008, Association for Computational Linguistics (ACL), pp. 43-48, 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15/07/18. https://doi.org/10.18653/v1/p18-2008

Active learning for deep semantic parsing. / Duong, Long; Afshar, Hadi; Estival, Dominique et al.
ACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers). ed. / Iryna Gurevych; Yusuke Miyao. Vol. 2 Association for Computational Linguistics (ACL), 2018. p. 43-48 P18-2008.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Active learning for deep semantic parsing

AU - Duong, Long

AU - Afshar, Hadi

AU - Estival, Dominique

AU - Pink, Glen

AU - Cohen, Philip

AU - Johnson, Mark

N1 - Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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 evaluate several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.

AB - 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 evaluate several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.

UR - http://www.scopus.com/inward/record.url?scp=85063133784&partnerID=8YFLogxK

U2 - 10.18653/v1/p18-2008

DO - 10.18653/v1/p18-2008

M3 - Conference proceeding contribution

AN - SCOPUS:85063133784

VL - 2

SP - 43

EP - 48

BT - ACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics

A2 - Gurevych, Iryna

A2 - Miyao, Yusuke

PB - Association for Computational Linguistics (ACL)

T2 - 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018

Y2 - 15 July 2018 through 20 July 2018

ER -

Duong L, Afshar H, Estival D, Pink G, Cohen P, Johnson M. Active learning for deep semantic parsing. In Gurevych I, Miyao Y, editors, ACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers). Vol. 2. Association for Computational Linguistics (ACL). 2018. p. 43-48. P18-2008 doi: 10.18653/v1/p18-2008

Active learning for deep semantic parsing

Abstract

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this