Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
This work addresses the challenge of efficiently leveraging crowdsourced data for training AI systems like personal assistants, representing an incremental improvement in active learning methods.
The paper tackles the problem of training deep learning models with noisy and sparse annotations from multiple crowdsourced annotators of unknown expertise, presenting a Bayesian framework that learns annotator expertise and infers true labels to reduce annotation requirements. Experiments on Alexa intent classification show the framework accurately learns expertise and reduces needed annotations compared to state-of-the-art methods.
This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expertise contribute an uncontrolled amount (often limited) of annotations. Our framework leverages the low-rank structure in annotations to learn individual annotator expertise, which then helps to infer the true labels from noisy and sparse annotations. It provides a unified Bayesian model to simultaneously infer the true labels and train the deep learning model in order to reach an optimal learning efficacy. Finally, our framework exploits the uncertainty of the deep learning model during prediction as well as the annotators' estimated expertise to minimize the number of required annotations and annotators for optimally training the deep learning model. We evaluate the effectiveness of our framework for intent classification in Alexa (Amazon's personal assistant), using both synthetic and real-world datasets. Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches. We further discuss the potential of our proposed framework in bridging machine learning and crowdsourcing towards improved human-in-the-loop systems.