A Survey of Active Learning for Natural Language Processing

Zhisong Zhang; Emma Strubell; Eduard Hovy

doi:10.18653/v1/2022.emnlp-main.414

A Survey of Active Learning for Natural Language Processing

Zhisong Zhang
, Emma Strubell
, Eduard Hovy

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

84 Citations (Scopus)

1 Downloads (CityUHK Scholars)

Abstract

In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions. © 2022 Association for Computational Linguistics.

Original language	English
Title of host publication	Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Editors	Yoav Goldberg , Zornitsa Kozareva, Yue Zhang
Publisher	Association for Computational Linguistics
Pages	6166-6190
Number of pages	25
ISBN (Print)	9781959429401
DOIs	https://doi.org/10.18653/v1/2022.emnlp-main.414
Publication status	Published - Dec 2022
Externally published	Yes
Event	2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) - Hybrid, Abu Dhabi, United Arab Emirates Duration: 7 Dec 2022 → 11 Dec 2022 https://2022.emnlp.org/ https://aclanthology.org/2022.emnlp-main https://aclanthology.org/2022.findings-emnlp

Publication series

Name	Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP

Conference

Conference	2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)
Place	United Arab Emirates
City	Abu Dhabi
Period	7/12/22 → 11/12/22
Internet address	https://2022.emnlp.org/ https://aclanthology.org/2022.emnlp-main https://aclanthology.org/2022.findings-emnlp

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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10.18653/v1/2022.emnlp-main.414

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Zhang, Z., Strubell, E., & Hovy, E. (2022). A Survey of Active Learning for Natural Language Processing. In Y. Goldberg , Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 6166-6190). (Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.414

Zhang, Zhisong ; Strubell, Emma ; Hovy, Eduard. / A Survey of Active Learning for Natural Language Processing. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. editor / Yoav Goldberg ; Zornitsa Kozareva ; Yue Zhang. Association for Computational Linguistics, 2022. pp. 6166-6190 (Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP).

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title = "A Survey of Active Learning for Natural Language Processing",

abstract = "In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions. {\textcopyright} 2022 Association for Computational Linguistics.",

author = "Zhisong Zhang and Emma Strubell and Eduard Hovy",

year = "2022",

month = dec,

doi = "10.18653/v1/2022.emnlp-main.414",

language = "English",

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publisher = "Association for Computational Linguistics",

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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",

address = "United States",

note = "2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) ; Conference date: 07-12-2022 Through 11-12-2022",

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Zhang, Z, Strubell, E & Hovy, E 2022, A Survey of Active Learning for Natural Language Processing. in Y Goldberg , Z Kozareva & Y Zhang (eds), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, Association for Computational Linguistics, pp. 6166-6190, 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Abu Dhabi, United Arab Emirates, 7/12/22. https://doi.org/10.18653/v1/2022.emnlp-main.414

A Survey of Active Learning for Natural Language Processing. / Zhang, Zhisong; Strubell, Emma; Hovy, Eduard.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. ed. / Yoav Goldberg ; Zornitsa Kozareva; Yue Zhang. Association for Computational Linguistics, 2022. p. 6166-6190 (Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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N2 - In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions. © 2022 Association for Computational Linguistics.

AB - In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions. © 2022 Association for Computational Linguistics.

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U2 - 10.18653/v1/2022.emnlp-main.414

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M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781959429401

T3 - Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP

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BT - Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

A2 - Goldberg , Yoav

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ER -

Zhang Z, Strubell E, Hovy E. A Survey of Active Learning for Natural Language Processing. In Goldberg Y, Kozareva Z, Zhang Y, editors, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2022. p. 6166-6190. (Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP). doi: 10.18653/v1/2022.emnlp-main.414

A Survey of Active Learning for Natural Language Processing

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