In this tutorial, we present you a portion of unique industry experience in efficient data labeling via crowdsourcing shared by both leading researchers and engineers from Yandex. Majority of ML projects require training data, and often this data can only be obtained by human labelling. Moreover, the more applications of AI appear, the more nontrivial tasks for collecting human labelled data arise. Production of such data on a large-scale requires construction of a technological pipeline, which includes solving issues related to quality control and smart distribution of tasks between performers.
We will make an introduction to data labelling via public crowdsourcing marketplaces and will present key components of efficient label collection. This will be followed by a practice session, where participants will choose one of the real label collection tasks, experiment with selecting settings for the labelling process, and launch their label collection project on Yandex.Toloka, one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session. Finally, participants will receive a feedback about their projects and practical advice to make them more efficient. We invite beginners, advanced specialists, and researchers to learn how to collect labelled data with good quality and do it efficiently.
Room: Sage
09:00 - 09:20 Introduction
— The concept of crowdsourcing
— Crowdsourcing task examples
— Crowdsourcing platforms
— Yandex's experience on crowdsourcing
09:20 - 10:00 Part I: Main components of data collection via crowdsourcing
— Decomposition for effective pipeline
— Task instruction & interface: best practices
— Quality control techniques
10:00 - 10:30 Coffee Break
10:55 - 11:05 Part III: Introduction to Yandex.Toloka for requesters
— Main types of instances
— Project: creation & configuration
— Pool: creation & configuration
— Tasks: uploading & golden set creation
— Statistics in flight and results downloading
11:05 - 12:30 Part IV: Setting up and running label collection projects (practice session)
— You
› create
› configure
› run data labelling projects on real performers in real-time
12:30 - 14:00 Lunch Break
14:35 - 15:00 Part VI: Theory on efficient aggregation
— Aggregation models
15:00 - 15:30 Coffee Break
16:50 - 17:00 Part IX: Discussion of results
from the projects and conclusions
— Results of your projects
— Ideas for further work and research
— References to literature and other tutorials