22,297 research outputs found
A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality
Microtask crowdsourcing is increasingly critical to the creation of extremely
large datasets. As a result, crowd workers spend weeks or months repeating the
exact same tasks, making it necessary to understand their behavior over these
long periods of time. We utilize three large, longitudinal datasets of nine
million annotations collected from Amazon Mechanical Turk to examine claims
that workers fatigue or satisfice over these long periods, producing lower
quality work. We find that, contrary to these claims, workers are extremely
stable in their quality over the entire period. To understand whether workers
set their quality based on the task's requirements for acceptance, we then
perform an experiment where we vary the required quality for a large
crowdsourcing task. Workers did not adjust their quality based on the
acceptance threshold: workers who were above the threshold continued working at
their usual quality level, and workers below the threshold self-selected
themselves out of the task. Capitalizing on this consistency, we demonstrate
that it is possible to predict workers' long-term quality using just a glimpse
of their quality on the first five tasks.Comment: 10 pages, 11 figures, accepted CSCW 201
Effect of Tuned Parameters on a LSA MCQ Answering Model
This paper presents the current state of a work in progress, whose objective
is to better understand the effects of factors that significantly influence the
performance of Latent Semantic Analysis (LSA). A difficult task, which consists
in answering (French) biology Multiple Choice Questions, is used to test the
semantic properties of the truncated singular space and to study the relative
influence of main parameters. A dedicated software has been designed to fine
tune the LSA semantic space for the Multiple Choice Questions task. With
optimal parameters, the performances of our simple model are quite surprisingly
equal or superior to those of 7th and 8th grades students. This indicates that
semantic spaces were quite good despite their low dimensions and the small
sizes of training data sets. Besides, we present an original entropy global
weighting of answers' terms of each question of the Multiple Choice Questions
which was necessary to achieve the model's success.Comment: 9 page
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