Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
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.
Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
Hammad Ayyubi, Rahul Lokesh, et al.
ACL 2023
Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021
Kevin Gu, Eva Tuecke, et al.
ICML 2024