Until these major breakthroughs happened recently, NLP approaches were built around static representations of words (word2vec). Static embeddings of a word, say “mouse”, would fare poorly in accounting for variance in the various contextualized representations of the word (as rodent or gadget). BERT, GPT-2 and ELMo changed all that – and in a big way.…

The Holy Grail for machine learning models is whether a model can infer causality, instead of finding correlations in data. Well, it isn’t like this is a big focus area among researchers currently, but it is a fascinating challenge. A researcher at Facebook, Leon Bottou, presented an interesting framework that shows a path forward. His…

It was a humbling moment for the state-of-the-art NLP models when an adversarial test compromised the output significantly. Yes, this included BERT as well, where its classification task prediction accuracy in a set of text analytics tasks reduced by 5 to 7 times! TextFooler is a baseline framework for synthetically creating adversarial samples, was created…