ALAMBIC: Active Learning Automation with Methods to Battle Inefficient Curation

Charlotte Nachtegael; Jacopo De Stefani; Tom Lenaerts

ALAMBIC: Active Learning Automation with Methods to Battle Inefficient Curation

Charlotte Nachtegael, Jacopo De Stefani, Tom Lenaerts

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

Abstract

In this paper, we present ALAMBIC, an open-source dockerized web-based platform for annotating text data through active learning for classification tasks. Active learning is known to reduce the need of labelling, a time-consuming task, by selecting the most informative instances among the unlabelled instances, reaching an optimal accuracy faster than by just randomly labelling data. ALAMBIC integrates all the steps from data import to customization of the (active) learning process and annotation of the data, with indications of the progress of the trained model that can be downloaded and used in downstream tasks. Its architecture also allows the easy integration of other types of models, features and active learning strategies. The code is available on https://trusted-ai-labs.github/ALAMBIC/ and a video demonstration is available on https://youtu.be/4oh8UADfEmY.

Original language	English
Title of host publication	EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations
Publisher	Association for Computational Linguistics (ACL)
Pages	117-127
Number of pages	11
ISBN (Electronic)	9781959429456
Publication status	Published - 2023
Event	17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Dubrovnik, Croatia Duration: 2 May 2023 → 4 May 2023

Publication series

Name	EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations

Conference

Conference	17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023
Country/Territory	Croatia
City	Dubrovnik
Period	2/05/23 → 4/05/23

Bibliographical note

Funding Information:
Basic architecture and design was largely inspired by the work done by Alexandre Renaux for ORVAL8 (Renaux et al., 2019). This work was supported by Service Public de Wallonie Recherche under grant n° 2010235 - ARIAC by DIGITAL-WALLONIA4.AI. We would also like to thank the anonymous reviewers for their helpful comments.

Publisher Copyright:
© 2023 Association for Computational Linguistics.

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

Cite this

Nachtegael, C., De Stefani, J., & Lenaerts, T. (2023). ALAMBIC: Active Learning Automation with Methods to Battle Inefficient Curation. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations (pp. 117-127). (EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations). Association for Computational Linguistics (ACL).

Nachtegael, Charlotte ; De Stefani, Jacopo ; Lenaerts, Tom. / ALAMBIC : Active Learning Automation with Methods to Battle Inefficient Curation. EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations. Association for Computational Linguistics (ACL), 2023. pp. 117-127 (EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations).

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abstract = "In this paper, we present ALAMBIC, an open-source dockerized web-based platform for annotating text data through active learning for classification tasks. Active learning is known to reduce the need of labelling, a time-consuming task, by selecting the most informative instances among the unlabelled instances, reaching an optimal accuracy faster than by just randomly labelling data. ALAMBIC integrates all the steps from data import to customization of the (active) learning process and annotation of the data, with indications of the progress of the trained model that can be downloaded and used in downstream tasks. Its architecture also allows the easy integration of other types of models, features and active learning strategies. The code is available on https://trusted-ai-labs.github/ALAMBIC/ and a video demonstration is available on https://youtu.be/4oh8UADfEmY.",

author = "Charlotte Nachtegael and {De Stefani}, Jacopo and Tom Lenaerts",

note = "Funding Information: Basic architecture and design was largely inspired by the work done by Alexandre Renaux for ORVAL8 (Renaux et al., 2019). This work was supported by Service Public de Wallonie Recherche under grant n° 2010235 - ARIAC by DIGITAL-WALLONIA4.AI. We would also like to thank the anonymous reviewers for their helpful comments. Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.; 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 ; Conference date: 02-05-2023 Through 04-05-2023",

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language = "English",

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Nachtegael, C, De Stefani, J & Lenaerts, T 2023, ALAMBIC: Active Learning Automation with Methods to Battle Inefficient Curation. in EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations. EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations, Association for Computational Linguistics (ACL), pp. 117-127, 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023, Dubrovnik, Croatia, 2/05/23.

ALAMBIC : Active Learning Automation with Methods to Battle Inefficient Curation. / Nachtegael, Charlotte; De Stefani, Jacopo; Lenaerts, Tom.

EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations. Association for Computational Linguistics (ACL), 2023. p. 117-127 (EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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T1 - ALAMBIC

T2 - 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023

AU - Nachtegael, Charlotte

AU - De Stefani, Jacopo

AU - Lenaerts, Tom

N1 - Funding Information: Basic architecture and design was largely inspired by the work done by Alexandre Renaux for ORVAL8 (Renaux et al., 2019). This work was supported by Service Public de Wallonie Recherche under grant n° 2010235 - ARIAC by DIGITAL-WALLONIA4.AI. We would also like to thank the anonymous reviewers for their helpful comments. Publisher Copyright: © 2023 Association for Computational Linguistics. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.

PY - 2023

Y1 - 2023

N2 - In this paper, we present ALAMBIC, an open-source dockerized web-based platform for annotating text data through active learning for classification tasks. Active learning is known to reduce the need of labelling, a time-consuming task, by selecting the most informative instances among the unlabelled instances, reaching an optimal accuracy faster than by just randomly labelling data. ALAMBIC integrates all the steps from data import to customization of the (active) learning process and annotation of the data, with indications of the progress of the trained model that can be downloaded and used in downstream tasks. Its architecture also allows the easy integration of other types of models, features and active learning strategies. The code is available on https://trusted-ai-labs.github/ALAMBIC/ and a video demonstration is available on https://youtu.be/4oh8UADfEmY.

AB - In this paper, we present ALAMBIC, an open-source dockerized web-based platform for annotating text data through active learning for classification tasks. Active learning is known to reduce the need of labelling, a time-consuming task, by selecting the most informative instances among the unlabelled instances, reaching an optimal accuracy faster than by just randomly labelling data. ALAMBIC integrates all the steps from data import to customization of the (active) learning process and annotation of the data, with indications of the progress of the trained model that can be downloaded and used in downstream tasks. Its architecture also allows the easy integration of other types of models, features and active learning strategies. The code is available on https://trusted-ai-labs.github/ALAMBIC/ and a video demonstration is available on https://youtu.be/4oh8UADfEmY.

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M3 - Conference paper

AN - SCOPUS:85159855604

T3 - EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations

SP - 117

EP - 127

BT - EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations

PB - Association for Computational Linguistics (ACL)

Y2 - 2 May 2023 through 4 May 2023

ER -

Nachtegael C, De Stefani J, Lenaerts T. ALAMBIC: Active Learning Automation with Methods to Battle Inefficient Curation. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations. Association for Computational Linguistics (ACL). 2023. p. 117-127. (EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations).

ALAMBIC: Active Learning Automation with Methods to Battle Inefficient Curation

Abstract

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Conference

Bibliographical note

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