ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP

TL;DR

This paper introduces ALE, a reproducible framework for empirically evaluating and comparing active learning strategies in NLP, aiding practitioners and researchers in making informed decisions and improving annotation efficiency.

Contribution

The paper presents a novel, easy-to-implement evaluation framework for comparing active learning strategies in NLP, addressing the lack of empirical performance data.

Findings

ALE enables low-effort, fair comparison of AL strategies

The framework supports reproducibility and customization of experiments

Case study demonstrates practical application of ALE

Abstract

Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.