  A popular approach for large scale data annotation tasks is crowdsourcing,
wherein each data point is labeled by multiple noisy annotators. We consider
the problem of inferring ground truth from noisy ordinal labels obtained from
multiple annotators of varying and unknown expertise levels. Annotation models
for ordinal data have been proposed mostly as extensions of their
binary/categorical counterparts and have received little attention in the
crowdsourcing literature. We propose a new model for crowdsourced ordinal data
that accounts for instance difficulty as well as annotator expertise, and
derive a variational Bayesian inference algorithm for parameter estimation. We
analyze the ordinal extensions of several state-of-the-art annotator models for
binary/categorical labels and evaluate the performance of all the models on two
real world datasets containing ordinal query-URL relevance scores, collected
through Amazon's Mechanical Turk. Our results indicate that the proposed model
performs better or as well as existing state-of-the-art methods and is more
resistant to `spammy' annotators (i.e., annotators who assign labels randomly
without actually looking at the instance) than popular baselines such as mean,
median, and majority vote which do not account for annotator expertise.
