When majority voting fails: Comparing quality assurance methods for noisy human computation environment
This addresses quality assurance issues for researchers and practitioners in human computation, offering incremental improvements over existing methods.
The paper tackles the problem of quality assurance in noisy human computation environments, showing that tournament and elimination selection methods using multi-way comparisons outperform majority voting in accuracy and reduce costs with 4-way comparisons.
Quality assurance remains a key topic in human computation research. Prior work indicates that majority voting is effective for low difficulty tasks, but has limitations for harder tasks. This paper explores two methods of addressing this problem: tournament selection and elimination selection, which exploit 2-, 3- and 4-way comparisons between different answers to human computation tasks. Our experimental results and statistical analyses show that both methods produce the correct answer in noisy human computation environment more often than majority voting. Furthermore, we find that the use of 4-way comparisons can significantly reduce the cost of quality assurance relative to the use of 2-way comparisons.