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

Lining Zhang, Simon Mille, Yufang Hou, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Saad Mahamood, Sebastian Gehrmann, Miruna Adriana Clinciu, Khyathi Raghavi Chandu, João Sedoc

Main: Resources and Evaluation Main-poster Paper

Poster Session 2: Resources and Evaluation (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 10, 14:00-15:30 (EDT) (America/Toronto)
Global Time: July 10, Poster Session 2 (18:00-19:30 UTC)
Keywords: evaluation, reproducibility, statistical testing for evaluation
TLDR: 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 Mecha...
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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.