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UNAM: Crowdsourcing natural language data

In this tutorial, leading researchers and engineers from Toloka share their unique industry experience in achieving efficient natural language annotation with crowdsourcing.

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Overview

In this tutorial, leading researchers and engineers from Toloka will share their unique industry experience in achieving efficient natural language annotation with crowdsourcing. We will introduce data labeling via public crowdsourcing marketplaces and present the key components of efficient label collection. Then, in the practice session, participants will choose one real language resource production task, experiment with selecting settings for the labeling process, and launch their label collection project on Toloka, one of the world’s largest crowdsourcing marketplaces. During the tutorial session, all projects will be run on the real Toloka crowd. We will also present useful quality control techniques and give the attendees an opportunity to discuss their own annotation ideas.

Topics

  • Reasons for collecting and labeling data via crowdsourcing for NLP
  • Key components of crowdsourcing for efficient data labeling
  • Decomposition approach
  • Performers selection and training
  • Hands-on practice session: audio transcription
  • Advanced crowdsourcing techniques: aggregation, incremental relabeling & pricing
  • Example of how to aggregate crowdsourced texts using the Crowd-Kit Python library

Speakers

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Dmitry Ustalov
TolokaAnalyst / Software Developer
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Daria Baidakova
TolokaDirector of Educational Programs
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Natalie Fedorova
TolokaEducational Project Manager

Organizers

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Natalie Fedorova
TolokaEducational Project Manager
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Aljona Johnson
TolokaEducational Project Manager
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Dr. Jesús Savage Carmona
UNAMFull-Time Professor C (Profesor de Tiempo Completo Titular C)

Schedule

10:00 – 10:15

Part 0: Introduction
— The concept of data labeling via crowdsourcing
— Crowdsourcing task examples
— Crowdsourcing platforms
— Yandex crowdsourcing experience

10:15 – 10:45

Part I: Key Components for Efficient Data Collection
— Decomposition for effective pipeline
— Task instruction & interface: best practices
— Quality control techniques

10:45 – 11:45

Part II: Practice part I
— Dataset and required labels
— Discussion: how to collect labels?
— Data labeling pipeline for implementation
— You
» create
» configure
» run data labeling projects on real performers in real-time

11:45 – 12:15

Break

12:15 – 12:40

Part III: Advanced techniques
— Incremental relabeling
— Dynamic pricing

12:40 – 13:00

Part IV: Practice part II
— Finishing up label collection
— Results aggregation

13:00 – 13:20

Part V: Conclusion
— Results of your projects
— Ideas for further work and research
— References to literature and other tutorials
— Q&A

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