ALAMBIC

Active Learning Automation with Methods to Battle Inefficient Curation

Conference paper (2023)

Authors

Charlotte Nachtegael Vrije Universiteit Brussel
Tom Lenaerts Vrije Universiteit Brussel
Copyright: © 2023 Charlotte Nachtegael, J. De Stefani, Tom Lenaerts
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Published Date

2023

Language

English

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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.

Files

2023.eacl_demo.14.pdf
(pdf | 1.23 Mb)
- Embargo expired in 15-11-2023
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