Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of the works on modeling the uncertainty of deep neural networks evaluate these methods on image classification tasks. Little attention has been paid to UE in natural language processing. To fill this gap, we perform a vast empirical investigation of state-of-the-art UE methods for Transformer models on misclassification detection in named entity recognition and text classification tasks and propose two computationally efficient modifications, one of which approaches or even outperforms computationally intensive methods.

We present ALToolbox – an open-source framework for active learning (AL) annotation in natural language processing. Currently, the framework supports text classification, sequence tagging, and seq2seq tasks. Besides state-of-the-art query strategies, ALToolbox provides a set of tools that help to reduce computational overhead and duration of AL iterations and increase annotated data reusability. The framework aims to support data scientists and researchers by providing an easy-to-deploy GUI annotation tool directly in the Jupyter IDE and an extensible benchmark for novel AL methods. We prepare a small demonstration of ALToolbox capabilities available a href=”http://demo.nlpresearch.group”online/a. A demo video for ALToolbox is provided at: a href=”http://demo-video.nlpresearch.group”http://demo-video.nlpresearch.group/a.The code of the framework is a href=”https://github.com/AIRI-Institute/al_toolbox”published/a under the MIT license.