Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer
TABLE I: Classification results with BERT+finetuning
['Training', 'Test', 'Accuracy', 'Precision', 'Recall', 'Specificity', 'F1- Score']
Starting from the checkpoint of BERT model, support-BERT was finetuned for 3 epochs. The finetuning was carried out in supervised learning framework for next sentence prediction. The finetuning for 3 epochs took 3 hours on our machine. In the test scenario for 1:1, accuracy increases up to 0.6966. In the more real-world scenario of 1:3 in test set, the accuracy increases up to 0.7228.