# Crowdsourced Labeling for Worker-Task Specialization Model

arXiv: https://arxiv.org/abs/2004.00101 · HTML: https://mlsanity.com/paper/2004.00101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits](https://mlsanity.com/paper/2111.12550/) (2021)

## Abstract (arXiv)

We consider crowdsourced labeling under a d-type worker-task specialization model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to tasks of unmatched types. We design an inference algorithm that recovers binary task labels (up to any given recovery accuracy) by using worker clustering, worker skill estimation and weighted majority voting. The designed inference algorithm does not require any information about worker/task types, and achieves any targeted recovery accuracy with the best known performance (minimum number of queries per task).