8,128 research outputs found
Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems
Crowdsourcing systems commonly face the problem of aggregating multiple
judgments provided by potentially unreliable workers. In addition, several
aspects of the design of efficient crowdsourcing processes, such as defining
worker's bonuses, fair prices and time limits of the tasks, involve knowledge
of the likely duration of the task at hand. Bringing this together, in this
work we introduce a new time--sensitive Bayesian aggregation method that
simultaneously estimates a task's duration and obtains reliable aggregations of
crowdsourced judgments. Our method, called BCCTime, builds on the key insight
that the time taken by a worker to perform a task is an important indicator of
the likely quality of the produced judgment. To capture this, BCCTime uses
latent variables to represent the uncertainty about the workers' completion
time, the tasks' duration and the workers' accuracy. To relate the quality of a
judgment to the time a worker spends on a task, our model assumes that each
task is completed within a latent time window within which all workers with a
propensity to genuinely attempt the labelling task (i.e., no spammers) are
expected to submit their judgments. In contrast, workers with a lower
propensity to valid labeling, such as spammers, bots or lazy labelers, are
assumed to perform tasks considerably faster or slower than the time required
by normal workers. Specifically, we use efficient message-passing Bayesian
inference to learn approximate posterior probabilities of (i) the confusion
matrix of each worker, (ii) the propensity to valid labeling of each worker,
(iii) the unbiased duration of each task and (iv) the true label of each task.
Using two real-world public datasets for entity linking tasks, we show that
BCCTime produces up to 11% more accurate classifications and up to 100% more
informative estimates of a task's duration compared to state-of-the-art
methods
From Task Classification Towards Similarity Measures for Recommendation in Crowdsourcing Systems
Task selection in micro-task markets can be supported by recommender systems
to help individuals to find appropriate tasks. Previous work showed that for
the selection process of a micro-task the semantic aspects, such as the
required action and the comprehensibility, are rated more important than
factual aspects, such as the payment or the required completion time. This work
gives a foundation to create such similarity measures. Therefore, we show that
an automatic classification based on task descriptions is possible.
Additionally, we propose similarity measures to cluster micro-tasks according
to semantic aspects.Comment: Work in Progress Paper at HCOMP 201
Near-Optimally Teaching the Crowd to Classify
How should we present training examples to learners to teach them
classification rules? This is a natural problem when training workers for
crowdsourcing labeling tasks, and is also motivated by challenges in
data-driven online education. We propose a natural stochastic model of the
learners, modeling them as randomly switching among hypotheses based on
observed feedback. We then develop STRICT, an efficient algorithm for selecting
examples to teach to workers. Our solution greedily maximizes a submodular
surrogate objective function in order to select examples to show to the
learners. We prove that our strategy is competitive with the optimal teaching
policy. Moreover, for the special case of linear separators, we prove that an
exponential reduction in error probability can be achieved. Our experiments on
simulated workers as well as three real image annotation tasks on Amazon
Mechanical Turk show the effectiveness of our teaching algorithm
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