DOI

  • Jie Yang
  • Thomas Drake
  • Andreas Damianou
  • Yoelle Maarek
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.
Original languageEnglish
Title of host publicationProceedings of the 2018 Worls Wide Web Conference, WWW 2018
Place of PublicationGeneva
Pages23-32
Number of pages10
ISBN (Electronic)978-1-4503-5639-8
DOIs
StatePublished - 2018
EventWWW 2018 - Lyon, France
Duration: 23 Apr 201827 Apr 2018
https://www2018.thewebconf.org

Conference

ConferenceWWW 2018
Abbreviated titleWWW 2018
CountryFrance
CityLyon
Period23/04/1827/04/18
Internet address

    Research areas

  • Deep Active Learning, Targeted Crowdsourcing, Bayesian Method

ID: 36754455