@inproceedings{purificato-etal-2026-select,
title = "Select, Label, Evaluate: Active Testing in {NLP}",
author = "Purificato, Antonio and
Bucarelli, Maria Sofia and
Bacciu, Andrea and
Silvestri, Fabrizio and
Mantrach, Amin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1160/",
pages = "26566--26584",
ISBN = "979-8-89176-459-0",
abstract = "Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly expensive due to the stringent requirement for low-error and high-quality labels necessary for reliable model evaluation. Traditional approaches require annotating entire test sets, leading to substantial resource requirements. Active Testing is a framework that selects the most informative test samples for annotation. Given a labeling budget, it aims to choose the subset that best estimates model performance while minimizing cost and human effort. In this work, we formalize Active Testing in NLP and we conduct an extensive benchmarking of existing approaches across 18 datasets and 4 embedding strategies spanning 4 different NLP tasks. The experiments show annotation reductions of up to 95{\%}, with performance estimation accuracy difference from the full test set within 1{\%}. Our analysis reveals variations in method effectiveness across different data characteristics and task types, with no single approach emerging as universally superior. Lastly, to address the limitation of requiring a predefined annotation budget in existing sample selection strategies, we introduce an adaptive stopping criterion that automatically determines the optimal number of samples. We release our code at https://github.com/amazon-science/NLPActiveTesting."
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%0 Conference Proceedings
%T Select, Label, Evaluate: Active Testing in NLP
%A Purificato, Antonio
%A Bucarelli, Maria Sofia
%A Bacciu, Andrea
%A Silvestri, Fabrizio
%A Mantrach, Amin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F purificato-etal-2026-select
%X Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly expensive due to the stringent requirement for low-error and high-quality labels necessary for reliable model evaluation. Traditional approaches require annotating entire test sets, leading to substantial resource requirements. Active Testing is a framework that selects the most informative test samples for annotation. Given a labeling budget, it aims to choose the subset that best estimates model performance while minimizing cost and human effort. In this work, we formalize Active Testing in NLP and we conduct an extensive benchmarking of existing approaches across 18 datasets and 4 embedding strategies spanning 4 different NLP tasks. The experiments show annotation reductions of up to 95%, with performance estimation accuracy difference from the full test set within 1%. Our analysis reveals variations in method effectiveness across different data characteristics and task types, with no single approach emerging as universally superior. Lastly, to address the limitation of requiring a predefined annotation budget in existing sample selection strategies, we introduce an adaptive stopping criterion that automatically determines the optimal number of samples. We release our code at https://github.com/amazon-science/NLPActiveTesting.
%U https://aclanthology.org/2026.emnlp-main.1160/
%P 26566-26584
Markdown (Informal)
[Select, Label, Evaluate: Active Testing in NLP](https://aclanthology.org/2026.emnlp-main.1160/) (Purificato et al., EMNLP 2026)
ACL
- Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu, Fabrizio Silvestri, and Amin Mantrach. 2026. Select, Label, Evaluate: Active Testing in NLP. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 26566–26584, Budapest, Hungary. Association for Computational Linguistics.