Frustratingly Easy Truth Discovery
Reshef Meir, Ofra Amir, Omer Ben-Porat, Tsviel Ben Shabat, Gal Cohensius, Lirong Xia
Abstract
Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources. For example, workers in a crowdsourcing platform. In this paper, we consider an extremely simple heuristic for estimating workers' competence using average proximity to other workers. We prove that this estimates well the actual competence level and enables separating high and low quality workers in a wide spectrum of domains and statistical models. Under Gaussian noise, this simple estimate is the unique solution to the MLE with a constant regularization factor. Finally, weighing workers according to their average proximity in a crowdsourcing setting, results in substantial improvement over unweighted aggregation and other truth discovery algorithms in practice.
BibTeX
@article{Meir_Amir_Ben-Porat_Ben Shabat_Cohensius_Xia_2023, title={Frustratingly Easy Truth Discovery}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/25750}, DOI={10.1609/aaai.v37i5.25750}, abstractNote={Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources. For example, workers in a crowdsourcing platform.
In this paper, we consider an extremely simple heuristic for estimating workers’ competence using average proximity to other workers. We prove that this estimates well the actual competence level and enables separating high and low quality workers in a wide spectrum of domains and statistical models. Under Gaussian noise, this simple estimate is the unique solution to the MLE with a constant regularization factor. Finally, weighing workers according to their average proximity in a crowdsourcing setting, results in substantial improvement over unweighted aggregation and other truth discovery algorithms in practice.}, number={5}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Meir, Reshef and Amir, Ofra and Ben-Porat, Omer and Ben Shabat, Tsviel and Cohensius, Gal and Xia, Lirong}, year={2023}, month={Jun.}, pages={6074-6083} }