Improving annotator selection in Active Learning using a mood and fatigue-aware Recommender System

TL;DR

This paper introduces a novel recommender system that considers annotator mood and fatigue to improve the selection process in active learning, reducing errors and enhancing model performance.

Contribution

It presents a new query-annotator pairing strategy that incorporates internal human factors, advancing active learning by accounting for mood and fatigue effects.

Findings

Reduced annotation errors with mood and fatigue-aware selection

Improved accuracy and F1-score in active learning models

Demonstrated the importance of internal factors in annotator performance

Abstract

This study centers on overcoming the challenge of selecting the best annotators for each query in Active Learning (AL), with the objective of minimizing misclassifications. AL recognizes the challenges related to cost and time when acquiring labeled data, and decreases the number of labeled data needed. Nevertheless, there is still the necessity to reduce annotation errors, aiming to be as efficient as possible, to achieve the expected accuracy faster. Most strategies for query-annotator pairs do not consider internal factors that affect productivity, such as mood, attention, motivation, and fatigue levels. This work addresses this gap in the existing literature, by not only considering how the internal factors influence annotators (mood and fatigue levels) but also presenting a new query-annotator pair strategy, using a Knowledge-Based Recommendation System (RS). The RS ranks the available annotators, allowing to choose one or more to label the queried instance using their past accuracy values, and their mood and fatigue levels, as well as information about the instance queried. This work bases itself on existing literature on mood and fatigue influence on human performance, simulating annotators in a realistic manner, and predicting their performance with the RS. The results show that considering past accuracy values, as well as mood and fatigue levels reduces the number of annotation errors made by the annotators, and the uncertainty of the model through its training, when compared to not using internal factors. Accuracy and F1-score values were also better in the proposed approach, despite not being as substantial as the aforementioned. The methodologies and findings presented in this study begin to explore the open challenge of human cognitive factors affecting AL.