A framework for evaluating and explaining the robustness of NLP models

The standard practice for evaluating the generalisation of supervised machine learning models in NLP tasks is to use previously unseen (i.e. held-out) data and report the performance on it using various metrics such as accuracy. Whilst metrics reported on held-out data summarise a model's performance, ultimately these results represent aggregate…