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Optimizing BERT with RoBERTa | Towards AI
A Robustly Optimized BERT Pretraining Approach
What is BERT?
BERT (Devlin et al., 2018) is a method of pre-training language representations, meaning that we train a general-purpose “language understanding” model on a large text corpus (like Wikipedia), and then use that model for downstream NLP tasks that we care about (like question answering). BERT outperforms previous methods because it is the first unsupervised, deeply bidirectional system for pre-training NLP.
Liu et al. studied the impact of many key hyper-parameters and training data size of BERT. They found that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. RoBERTa (Robustly optimized BERT approach) is introduced and performance is either matching or exceeding original BERT.
BERT Training Objective
BERT uses Masked Language Models (MLM) and Next Sentence Prediction (NSP) to learn text representation. MLM is a way to mask some tokens and using the rest of tokens to predict the masked token. NSP is predicting whether a pair of sentences is continuous. If you want to learn more about BERT, you may visit this story.
