ALANNO: An Active Learning Annotation System for Mortals
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Abstract (arXiv)
Supervised machine learning has become the cornerstone of today's data-driven society, increasing the need for labeled data. However, the process of acquiring labels is often expensive and tedious. One possible remedy is to use active learning (AL) -- a special family of machine learning algorithms designed to reduce labeling costs. Although AL has been successful in practice, a number of practical challenges hinder its effectiveness and are often overlooked in existing AL annotation tools. To address these challenges, we developed ALANNO, an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects. ALANNO facilitates annotation management in a multi-annotator setup and supports a variety of AL methods and underlying models, which are easily configurable and extensible.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 47 references that are in the map. First posted on arXiv 11 Nov 2022.
Builds on
12 of 47 references.
References · 12
- 2017Attention Is All You Needcited by 59,932
- 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
- 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
- 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 370
- 2018Practical Obstacles to Deploying Active Learningcited by 68
- 2019Sampling Bias in Deep Active Classification: An Empirical Studycited by 36
- 2018modAL: A modular active learning framework for Pythoncited by 35
- 2014A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stoppingcited by 21
- 2021Small-Text: Active Learning for Text Classification in Pythoncited by 18
- 2019ALiPy: Active Learning in Pythoncited by 11
- 2022Label Sleuth: From Unlabeled Text to a Classifier in a Few Hourscited by 8
- 2020Deconstructing word embedding algorithmscited by 5
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