Crowdsourcing

Crowdsourcing Services

A range of services exist for collecting annotations from paid workers. This post gives an overview of a bunch of them.

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time (Huang et al., 2018)

For a more flexible dialogue system, use the crowd to propose and vote on responses, then introduce agents and a model for voting, gradually learning to replace the crowd.

Real-time Captioning by Groups of Non-experts (Lasecki et al., 2012)

By dividing a task up among multiple annotators carefully we can achieve high-quality real-time annotation of data, in this case transcription of audio.

Learning Whom to Trust with MACE (Hovy et al., NAACL 2013)

By using a generative model to explain worker annotations, we can more effectively predict the correct label, and which workers are spamming.