BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Paper Explained)

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This video explains a legendary paper, BERT. It leverages the Transformer encoder and comes up with an innovative way to pre-training language models (masked language modeling). BERT has a significant influence on how people approach NLP problems and inspires a lot of following studies and BERT variants.

0:00 - Intro 1:32 - Transformer v.s LSTMs 3:34 - Pre-BERT times 8:22 - Model architecture 9:46 - WordPiece embeddings 14:25 - Special tokens 16:42 - Input representations 18:15 - Masked language modeling 20:03 - Mismatch between pre-training and fine-tuning 23:21 - Next sentence prediction 26:28 - Pre-training data 30:57 - end-to-end fine-tuning 34:45 - SQaUD 36:57 - Ablation over pre-training tasks 41:37 - Ablation over model size 43:17 - Feature-based approach with BERT

Related Videos: Transformer explained https://youtu.be/ELTGIye424E

Introduction of GPT-3: The Most Powerful Language Model Ever https://youtu.be/Rv5SeM7LxLQ

Paper https://arxiv.org/abs/1810.04805

Code https://github.com/google-research/bert (TensorFlow) https://github.com/huggingface/transformers (PyTorch)

Connect Twitter https://twitter.com/home email edwindeeplearning@gmail.com

Abstract We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement). ... https://www.youtube.com/watch?v=j9toSIRf4RI

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2021-01-02

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