BERT

What is BERT?

BERT, which stands for Bidirectional Encoder Representations from Transformers, is an open-source machine learning framework for natural language processing (NLP). Developed by researchers at Google AI Language and introduced in 2018, BERT has significantly advanced NLP by enabling machines to understand language more like humans do.

At its core, BERT helps computers interpret the meaning of ambiguous or context-dependent language in text by considering surrounding words in a sentence—both before and after the target word. This bidirectional approach allows BERT to grasp the full nuance of language, making it highly effective for a wide variety of NLP tasks.

Background and History of BERT

The Evolution of Language Models

Before BERT, most language models processed text in a unidirectional manner (either left-to-right or right-to-left), which limited their ability to capture context.

Earlier models like Word2Vec and GloVe generated context-free word embeddings, assigning a single vector to each word regardless of context. This approach struggled with polysemous words (e.g., “bank” as a financial institution vs. riverbank).

The Introduction of Transformers

In 2017, the Transformer architecture was introduced in the paper “Attention Is All You Need.” Transformers are deep learning models that use self-attention, allowing them to weigh the significance of each part of the input dynamically.

Transformers revolutionized NLP by processing all words in a sentence simultaneously, enabling larger-scale training.

Development of BERT

Google researchers built on the Transformer architecture to develop BERT, introduced in the 2018 paper “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” BERT’s innovation was applying bidirectional training, considering both left and right context.

BERT was pretrained on the entire English Wikipedia (2.5 billion words) and BookCorpus (800 million words), giving it a deep understanding of patterns, syntax, and semantics.

Architecture of BERT

Overview

BERT is an encoder stack of the Transformer architecture (uses only the encoder, not the decoder). It consists of multiple layers (12 or 24 Transformer blocks), each with self-attention and feed-forward neural networks.

Tokenization and Embedding

BERT uses WordPiece tokenization, breaking words into subword units to handle rare/out-of-vocabulary words.

Each input token is represented by the sum of three embeddings:

1. Token Embeddings: Individual tokens (words or subwords).
2. Segment Embeddings: Indicate if a token belongs to sentence A or B.
3. Position Embeddings: Provide positional information for each token.

These help BERT understand both structure and semantics.

Self-Attention Mechanism

Self-attention lets BERT weigh the importance of each token relative to all others in the sequence, capturing dependencies regardless of their distance.

For example, in “The bank raised its interest rates,” self-attention helps BERT link “bank” to “interest rates,” understanding “bank” as a financial institution.

Bidirectional Training

BERT’s bidirectional training enables it to capture context from both directions. This is achieved through two training objectives:

1. Masked Language Modeling (MLM): Randomly masks input tokens and trains BERT to predict them based on context.
2. Next Sentence Prediction (NSP): Trains BERT to predict if sentence B follows sentence A, helping it understand sentence relationships.

How BERT Works

Masked Language Modeling (MLM)

In MLM, BERT randomly selects 15% of tokens for possible replacement:

• 80% replaced with [MASK]
• 10% replaced with a random token
• 10% left unchanged

This strategy encourages deeper language understanding.

Example:

• Original: “The quick brown fox jumps over the lazy dog.”
• Masked: “The quick brown [MASK] jumps over the lazy [MASK].”
• Model predicts “fox” and “dog.”

Next Sentence Prediction (NSP)

NSP helps BERT understand relationships between sentences.

• 50% of the time, sentence B is the true next sentence.
• 50% of the time, sentence B is random from the corpus.

Examples:

• Sentence A: “The rain was pouring down.”
• Sentence B: “She took out her umbrella.” → “IsNext”
• Sentence B: “I enjoy playing chess.” → “NotNext”

Fine-Tuning for Downstream Tasks

After pretraining, BERT is fine-tuned for specific NLP tasks by adding output layers. Fine-tuning requires less data and compute than training from scratch.

How BERT Is Used

BERT powers many NLP tasks, often achieving state-of-the-art results.

Sentiment Analysis

BERT can classify sentiment (e.g., positive/negative reviews) with subtlety.

• Example: E-commerce uses BERT to analyze reviews and improve products.

Question Answering

BERT understands questions and provides answers from context.

• Example: A chatbot uses BERT to answer “What is the return policy?” by referencing policy documents.

Named Entity Recognition (NER)

NER identifies and classifies key entities (names, organizations, dates).

• Example: News aggregators extract entities for users to search specific topics.

Language Translation

While not designed for translation, BERT’s deep language understanding aids translation when combined with other models.

Text Summarization

BERT can generate concise summaries by identifying key concepts.

• Example: Legal firms summarize contracts for quick information access.

Text Generation and Completion

BERT predicts masked words or sequences, aiding text generation.

• Example: Email clients suggest next words as users type.

Examples of Use Cases

Google Search

In 2019, Google began using BERT to improve search algorithms, understanding context and intent behind queries.

Example:

• Search Query: “Can you get medicine for someone pharmacy?”
• With BERT: Google understands the user is asking about picking up medicine for someone else.

AI Automation and Chatbots

BERT powers chatbots, improving understanding of user input.

• Example: Customer support chatbots use BERT to handle complex questions without human help.

Healthcare Applications

Specialized BERT models like BioBERT process biomedical texts.

• Example: Researchers use BioBERT for drug discovery and literature analysis.

Legal Document Analysis

Legal professionals use BERT to analyze and summarize legal texts.

• Example: Law firms identify liability clauses faster with BERT.

Variations and Extensions of BERT

Several BERT adaptations exist for efficiency or specific domains:

• DistilBERT: Smaller, faster, lighter, with 95% of BERT’s performance using 40% fewer parameters.
  Use Case: Mobile environments.
• TinyBERT: Even more condensed, reducing model size and inference time.
• RoBERTa: Trained with larger batches and more data, omitting NSP, achieving even better performance.
• BioBERT: Pretrained on biomedical texts for biomedical NLP.
• PatentBERT: Fine-tuned for patent classification.
• SciBERT: Tailored for scientific text.
• VideoBERT: Integrates visual and textual data for video understanding.

BERT in AI, AI Automation, and Chatbots

Enhancing AI Applications

BERT’s contextual understanding powers numerous AI applications:

• Improved Language Understanding: Interprets text with nuance and context.
• Efficient Transfer Learning: Pretrained models fine-tuned with little data.
• Versatility: Reduces need for task-specific models.

Impact on Chatbots

BERT has greatly improved chatbot and AI automation quality.

Examples:

• Customer Support: Chatbots understand and respond accurately.
• Virtual Assistants: Better command recognition and response.
• Language Translation Bots: Maintains context and accuracy.

AI Automation

BERT enables AI automation for processing large text volumes without human intervention.

Use Cases:

• Document Processing: Automated sorting, tagging, and summarization.
• Content Moderation: Identifying inappropriate content.
• Automated Reporting: Extracting key information for reports.

Research on BERT

1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
   Authors: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
   Introduces BERT’s architecture and effectiveness on multiple benchmarks, enabling joint conditioning on both left and right context.
   Read more

2. Multi-Task Bidirectional Transformer Representations for Irony Detection
   Authors: Chiyu Zhang, Muhammad Abdul-Mageed
   Applies BERT to irony detection, leveraging multi-task learning and pretraining for domain adaptation. Achieves 82.4 macro F1 score.
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3. Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
   Authors: Hangyu Lin, Yanwei Fu, Yu-Gang Jiang, Xiangyang Xue
   Introduces Sketch-BERT for sketch recognition and retrieval, applying self-supervised learning and novel embedding networks.
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4. Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching
   Author: Piotr Rybak
   Proposes vocabulary matching to adapt BERT for low-resource languages, democratizing NLP technology.
   Read more