Transfer Learning Method 1

Transfer Learning Method 2

A simple method for designing a self-supervised classification task to train an encoder is Next Sentence Prediction (NSP), as presented in the original BERT paper. This approach is built on the assumption that a good text encoder should effectively capture the relationship between two sentences. To model this, NSP uses the output of encoding two consecutive sentences, $$\mathrm{Sent}_{A}$$ and $$\mathrm{Sent}_{B}$$, to determine whether $$\mathrm{Sent}_{B}$$ is indeed the next sentence following $$\mathrm{Sent}_{A}$$. For example, if $$\mathrm{Sent}_{A}$$ is 'It is raining .' and $$\mathrm{Sent}_{B}$$ is 'I need an umbrella .', the model is tasked with recognizing this sequential relationship.

Next Sentence Prediction (NSP)

State University of New York at Stony Brook

 $\textbf{B}$idirectional $\textbf{E}$ncoder $\textbf{R}$epresentations from $\textbf{T}$ransformers
- A new language representation model that is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers
- Beneficial because it is a generalist approach that allows for customization to specific tasks (question answering, language inference, etc) by adding just one additional layer, while other approaches might build entirely different models for different language-processing tasks

What is BERT?

This paper presents BERT fine-tuning results on 11 Natural Language Processing (NLP) tasks.

BERT Experiments

BERT&GPT and Fine Tuning

BERT models are built to represent either individual sentences or pairs of sentences, equipping them to tackle a variety of downstream language understanding problems. When a task requires handling two sentences simultaneously, the input is arranged as a single combined sequence containing both sentences, typically denoted as $$\mathrm{Sent}_{A}$$ and $$\mathrm{Sent}_{B}$$.

BERT Input Representation: Single and Paired Sentences

BERT is recognized as a milestone model in NLP that has inspired significant subsequent research. Its key contributions include demonstrating the importance of bidirectional pre-training for language representations, which was enabled by the masked language model. BERT also showed that pre-trained representations reduce the need for heavily-engineered, task-specific architectures. It was the first fine-tuning based representation model to achieve state-of-the-art performance on a wide range of sentence-level and token-level tasks, advancing the state of the art for eleven NLP tasks.

BERT's Contributions and Impact

As proposed in the original paper by Devlin et al. (2019), the standard BERT model is a Transformer encoder pre-trained with a dual-task objective. This training process involves simultaneously learning from two tasks: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). The total training loss is calculated as the sum of the individual losses from these two objectives.

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