  Using a taxonomy to organize information requires classifying objects
(documents, images, etc) with appropriate taxonomic classes. The flexible
nature of zero-shot learning is appealing for this task because it allows
classifiers to naturally adapt to taxonomy modifications. This work studies
zero-shot multi-label document classification with fine-tuned language models
under realistic taxonomy expansion scenarios in the human resource domain.
Experiments show that zero-shot learning can be highly effective in this
setting. When controlling for training data budget, zero-shot classifiers
achieve a 12% relative increase in macro-AP when compared to a traditional
multi-label classifier trained on all classes. Counterintuitively, these
results suggest in some settings it would be preferable to adopt zero-shot
techniques and spend resources annotating more documents with an incomplete set
of classes, rather than spreading the labeling budget uniformly over all
classes and using traditional classification techniques. Additional experiments
demonstrate that adopting the well-known filter/re-rank decomposition from the
recommender systems literature can significantly reduce the computational
burden of high-performance zero-shot classifiers, empirically resulting in a
98% reduction in computational overhead for only a 2% relative decrease in
performance. The evidence presented here demonstrates that zero-shot learning
has the potential to significantly increase the flexibility of taxonomies and
highlights directions for future research.
