Substituting Data Annotation with Balanced Updates and Collective Loss
  in Multi-label Text Classification
Muberra Ozmen Joseph Cotnareanu Mark Coates
Multi-label text classification (MLTC) is the task of assigning multiple
labels to a given text, and has a wide range of application domains. Most
existing approaches require an enormous amount of annotated data to learn a
classifier and/or a set of well-defined constraints on the label space
structure, such as hierarchical relations which may be complicated to provide
as the number of labels increases. In this paper, we study the MLTC problem in
annotation-free and scarce-annotation settings in which the magnitude of
available supervision signals is linear to the number of labels. Our method
follows three steps, (1) mapping input text into a set of preliminary label
likelihoods by natural language inference using a pre-trained language model,
(2) calculating a signed label dependency graph by label descriptions, and (3)
updating the preliminary label likelihoods with message passing along the label
dependency graph, driven with a collective loss function that injects the
information of expected label frequency and average multi-label cardinality of
predictions. The experiments show that the proposed framework achieves
effective performance under low supervision settings with almost imperceptible
computational and memory overheads added to the usage of pre-trained language
model outperforming its initial performance by 70\% in terms of example-based
F1 score.