Build a Smart Question Answering System with Fine-Tuned BERT

At the end of 2018, researchers at Google AI Language open-sourced a new technique for Natural Language Processing (NLP) called BERT (Bidirectional Encoder Representations from Transformers). BERT exhibited unprecedented performance for modelling language-based tasks. In this blog post, we are going to understand how we can apply a fine-tuned BERT to question answering tasks i.e given a question and a passage containing the answer, the task is to predict the answer text span in the passage. BERT uses Transformer encoder blocks. The transformer encoder uses attention (Multi-Headed Self Attention) mechanism that learns contextual relations between words (or sub-words) in text.