Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization

TL;DR

This paper presents a two-step pipeline for recruiting high-quality MTurk annotators for summarization evaluation, successfully filtering out low-quality workers and achieving high agreement, though alignment with experts remains challenging.

Contribution

Introduces a method to effectively filter and recruit dependable MTurk workers for complex annotation tasks like summarization evaluation.

Findings

High agreement among recruited workers and CloudResearch workers.

Filtering improves annotation quality and resource efficiency.

Alignment with expert judgments requires further training.

Abstract

To prevent the costly and inefficient use of resources on low-quality annotations, we want a method for creating a pool of dependable annotators who can effectively complete difficult tasks, such as evaluating automatic summarization. Thus, we investigate the recruitment of high-quality Amazon Mechanical Turk workers via a two-step pipeline. We show that we can successfully filter out subpar workers before they carry out the evaluations and obtain high-agreement annotations with similar constraints on resources. Although our workers demonstrate a strong consensus among themselves and CloudResearch workers, their alignment with expert judgments on a subset of the data is not as expected and needs further training in correctness. This paper still serves as a best practice for the recruitment of qualified annotators in other challenging annotation tasks.