# Mix and Match: Collaborative Expert-Crowd Judging for Building Test Collections Accurately and Affordably

arXiv: https://arxiv.org/abs/1806.00755 · HTML: https://mlsanity.com/paper/1806.00755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

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

- [The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims](https://mlsanity.com/paper/2202.08901/) (2022)

## Abstract (arXiv)

Crowdsourcing offers an affordable and scalable means to collect relevance judgments for IR test collections. However, crowd assessors may show higher variance in judgment quality than trusted assessors. In this paper, we investigate how to effectively utilize both groups of assessors in partnership. We specifically investigate how agreement in judging is correlated with three factors: relevance category, document rankings, and topical variance. Based on this, we then propose two collaborative judging methods in which a portion of the document-topic pairs are assessed by in-house judges while the rest are assessed by crowd-workers. Experiments conducted on two TREC collections show encouraging results when we distribute work intelligently between our two groups of assessors.