The major focus of this work is to investigate the scalability of our approach on datasets with a large output space, and generalizability in low-resource settings with few training examples.
we validate our capsule-based approach on two specific NLP tasks: (i) multi-label text classification with a large label scale; (ii) question answering with a data imbalance issue.