Three Important Things
1. Masked Language Modeling
A challenge of training the deep bidirectional model is that it cannot be done either left-to-right or right-to-left, since the bidirectional nature of the Transformer will allow it to see the tokens in advance.
To resolve this, the authors introduced “masked LM” (MLM), where tokens are masked at random and the task is to predict the masked tokens. This is the first task used in their pre-training phase, the other being next sentence prediction (NSP).
2. Next Sentence Prediction
To ensure that the language model understands the relationships between sentences, it is also pre-trained on next sentence prediction tasks.
3. Robust on Both Fine-tuning and Feature Extraction
Ablation studies shows that BERT performs competitively with both fine-tuning on downstream tasks, and on using frozen feature extractions with a biLSTM layer on top.
Most Glaring Deficiency
In the setup for next sentence prediction, it was unclear to me how the embeddings for
NotNext were chosen and what the loss function is such that \(C\) has some useful representation.
This is particularly pertinent because of the importance of \(C\) for downstream tasks.
Conclusions for Future Work
To take advantage of bi-directionality in sequential data, we can apply ideas similar to masked language modeling even for data of other modalities.