BERT and Google
BERT (Bidirectional Encoder Representations from Transformers) was a groundbreaking language representation model that revolutionised the field of natural language processing (NLP) when it was introduced in 2018.
The key innovation of BERT was its ability to learn contextual representations of words by jointly conditioning on both the left and right context in all layers of the model.
This bidirectional nature allowed BERT to capture rich semantic information and understand the complex relationships between words in a sentence.
Unlike previous models that only analysed text in one direction, BERT processed words in relation to all other words in a sentence, allowing for a deep, bidirectional understanding of context.
BERT was integrated into Google Search in 2019 to enhance its understanding of the nuances and context of user queries, significantly improving the quality of search results.
Prior to BERT, Google Search mainly relied on keyword matching to retrieve relevant results.
BERT's deep bidirectional understanding allowed Google to grasp the intent behind queries more effectively, especially for longer, more conversational phrases or when the query uses prepositions like "for" and "to," which can alter the meaning.
Model Architecture
BERT uses a multi-layer bidirectional Transformer encoder as its base architecture.
The input representation is constructed by summing the token embeddings, segment embeddings, and position embeddings.
The token embeddings are the learned representations of each token, the segment embeddings distinguish between sentence pairs, and the position embeddings capture the relative position of each token.
Pretraining
BERT is pre-trained on a large corpus of unlabelled text using two unsupervised tasks: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).
Masked Language Model (MLM)
The MLM task is designed to enable bidirectional representation learning in BERT.
The main idea is to randomly mask some percentage of the input tokens and then predict those masked tokens based on the context provided by the unmasked tokens.
This allows the model to learn from both the left and right context of a masked token.
Here's how the MLM task works:
Randomly select 15% of the tokens in the input sequence.
For each selected token, perform the following:
80% of the time: Replace the token with the special [MASK] token.
10% of the time: Replace the token with a random token from the vocabulary.
10% of the time: Keep the token unchanged.
Feed the modified sequence into the BERT model.
Predict the original token for each masked position using the final hidden vectors corresponding to the masked tokens.
Calculate the cross-entropy loss between the predicted tokens and the original tokens.
Example:
Original sentence: "The quick brown fox jumps over the lazy dog."
Masked sentence: "The quick [MASK] fox [MASK] over the lazy [MASK]."
In this example, the words "brown," "jumps," and "dog" are masked. The model then predicts the original words based on the context provided by the unmasked tokens.
By masking tokens randomly, BERT learns to predict words based on both the left and right context, allowing it to learn bidirectional representations.
Next Sentence Prediction (NSP)
The NSP task is designed to capture the relationship between two sentences, which is important for downstream tasks like Question Answering (QA) and Natural Language Inference (NLI).
The goal is to predict whether the second sentence follows the first sentence in the original text.
Here's how the NSP task works:
For each pre-training example, choose two sentences (A and B) from the corpus.
50% of the time, B is the actual next sentence that follows A (labeled as IsNext).
50% of the time, B is a random sentence from the corpus (labeled as NotNext).
Feed the sentence pair (A and B) into the BERT model, separated by the special [SEP] token.
Use the final hidden vector corresponding to the [CLS] token (C in Figure 1 of the paper) for binary classification (IsNext or NotNext).
Example:
Sentence A: "The quick brown fox jumps over the lazy dog."
Sentence B (IsNext): "The dog was not amused by the fox's antics."
Sentence B (NotNext): "Penguins are flightless birds found in the Southern Hemisphere."
In this example, the sentence pair (A, B) with "IsNext" label represents a coherent sequence, while the sentence pair (A, B) with "NotNext" label is not coherent.
By training on the Next Sentence Prediction task, BERT learns to understand the relationship between sentences.
During pre-training, BERT is trained on a large corpus (e.g., BooksCorpus and Wikipedia) using these two tasks.
The model learns to capture the contextual representations of words by attending to both the left and right context in all layers.
Input Representation
To perform these tasks, BERT takes an input sequence of tokens and constructs an input representation by summing the token embeddings, segment embeddings, and position embeddings.
As per the diagram below:
Token embeddings: Learned embeddings for each token in the input sequence.
Segment embeddings: Embeddings indicating which sentence each token belongs to (sentence A or B).
Position embeddings: Embeddings representing the relative position of each token in the sequence.
This input representation allows BERT to capture both the token-level information and the sentence-level structure during pre-training.
Fine-tuning
After pre-training, BERT can be fine-tuned on specific downstream tasks with minimal task-specific architecture changes.
Input Representation
The input sequence is tokenized and converted into input embeddings.
The input embeddings are the sum of token embeddings, segment embeddings (to differentiate sentences), and position embeddings (to capture positional information).
Task-Specific Output Layer
For each downstream task, a task-specific output layer is added on top of the pre-trained BERT model.
For sequence-level classification tasks (e.g., sentiment analysis), the output layer is typically a simple classifier that takes the representation of the [CLS] token (the first token of the sequence) as input.
For token-level tasks (e.g., named entity recognition), the output layer is applied to each token's representation to make predictions.
Fine-tuning Procedure
The pre-trained BERT model is initialised with the pre-trained weights.
The task-specific output layers are added on top of the pre-trained model.
The entire model (pre-trained BERT + task-specific layers) is fine-tuned on the labeled data for the downstream task.
The model is trained using standard supervised learning techniques, such as cross-entropy loss for classification tasks.
The fine-tuning process typically involves a smaller learning rate and fewer training epochs compared to pre-training.
By leveraging the pre-trained representations learned during the unsupervised pre-training phase, BERT can achieve state-of-the-art performance on a wide range of downstream tasks with minimal task-specific architecture changes and fine-tuning.
The fine-tuning approach allows BERT to adapt its general language understanding capabilities to specific tasks, making it a powerful and versatile model for various natural language processing applications.
Experimental Results
The authors evaluate BERT on a wide range of natural language processing tasks, including sentence-level tasks (e.g., natural language inference, paraphrasing) and token-level tasks (e.g., named entity recognition, question answering).
BERT achieves state-of-the-art results on eleven tasks, demonstrating its effectiveness as a pre-training approach.
Conclusion and Impact of BERT
BERT (Bidirectional Encoder Representations from Transformers) represented a significant breakthrough in the field of natural language processing (NLP).
The fundamental innovation of BERT was in its ability to pre-train deep bidirectional representations from unlabelled text by jointly conditioning on both left and right context in all layers.
The bidirectional nature of BERT allows it to capture rich contextual information from both directions, making it particularly well-suited for understanding the complex relationships between words and sentences.
This is important for tasks such as question answering, where the model needs to consider the entire context to provide accurate answers.
Another key innovation of BERT is the use of the Transformer architecture, which enables efficient parallel training and captures long-range dependencies in text.
The Transformer's self-attention mechanism allows each word to attend to all other words in the input sequence, enabling the model to learn more expressive and nuanced representations.
The impact of BERT has been significant in both academia and industry.
In real-world applications, BERT has been widely adopted by companies and organisations to improve their NLP systems.
For example, Google has used BERT to enhance its search engine results, Microsoft has integrated BERT into its Bing search engine and Office products, and many chatbot and virtual assistant platforms have leveraged BERT to improve their conversational capabilities.
Moreover, BERT has inspired numerous variants and extensions, such as RoBERTa, ALBERT, and DistilBERT, which aim to improve upon the original model in terms of performance, efficiency, and scalability.
The success of BERT has also paved the way for more advanced language models, which have pushed the boundaries of what is possible with NLP.
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