  In this paper, we investigate the effect of addressing difficult samples from
a given text dataset on the downstream text classification task. We define
difficult samples as being non-obvious cases for text classification by
analysing them in the semantic embedding space; specifically - (i) semantically
similar samples that belong to different classes and (ii) semantically
dissimilar samples that belong to the same class. We propose a penalty function
to measure the overall difficulty score of every sample in the dataset. We
conduct exhaustive experiments on 13 standard datasets to show a consistent
improvement of up to 9% and discuss qualitative results to show effectiveness
of our approach in identifying difficult samples for a text classification
model.
