  Crowdsourcing has emerged as a powerful paradigm for efficiently labeling
large datasets and performing various learning tasks, by leveraging crowds of
human annotators. When additional information is available about the data,
semi-supervised crowdsourcing approaches that enhance the aggregation of labels
from human annotators are well motivated. This work deals with semi-supervised
crowdsourced classification, under two regimes of semi-supervision: a) label
constraints, that provide ground-truth labels for a subset of data; and b)
potentially easier to obtain instance-level constraints, that indicate
relationships between pairs of data. Bayesian algorithms based on variational
inference are developed for each regime, and their quantifiably improved
performance, compared to unsupervised crowdsourcing, is analytically and
empirically validated on several crowdsourcing datasets.
