  Quantification is a supervised learning task that consists in predicting,
given a set of classes C and a set D of unlabelled items, the prevalence (or
relative frequency) p(c|D) of each class c in C. Quantification can in
principle be solved by classifying all the unlabelled items and counting how
many of them have been attributed to each class. However, this "classify and
count" approach has been shown to yield suboptimal quantification accuracy;
this has established quantification as a task of its own, and given rise to a
number of methods specifically devised for it. We propose a recurrent neural
network architecture for quantification (that we call QuaNet) that observes the
classification predictions to learn higher-order "quantification embeddings",
which are then refined by incorporating quantification predictions of simple
classify-and-count-like methods. We test {QuaNet on sentiment quantification on
text, showing that it substantially outperforms several state-of-the-art
baselines.
