  Practical sequence classification tasks in natural language processing often
suffer from low training data availability for target classes. Recent works
towards mitigating this problem have focused on transfer learning using
embeddings pre-trained on often unrelated tasks, for instance, language
modeling. We adopt an alternative approach by transfer learning on an ensemble
of related tasks using prototypical networks under the meta-learning paradigm.
Using intent classification as a case study, we demonstrate that increasing
variability in training tasks can significantly improve classification
performance. Further, we apply data augmentation in conjunction with
meta-learning to reduce sampling bias. We make use of a conditional generator
for data augmentation that is trained directly using the meta-learning
objective and simultaneously with prototypical networks, hence ensuring that
data augmentation is customized to the task. We explore augmentation in the
sentence embedding space as well as prototypical embedding space. Combining
meta-learning with augmentation provides upto 6.49% and 8.53% relative F1-score
improvements over the best performing systems in the 5-shot and 10-shot
learning, respectively.
