[AI from Scratch] Episode 196: BERT and the Masked Language Model — Learning Mechanisms of BERT

Recap: Positional Encoding

In the previous episode, we discussed Positional Encoding in the Transformer model. Positional Encoding is a technique that handles the sequence information of words, playing a crucial role in helping the Transformer model understand the order and context of words. We explored two methods: Absolute Positional Encoding and Relative Positional Encoding, understanding their characteristics and advantages. This time, we’ll focus on BERT (Bidirectional Encoder Representations from Transformers) and its learning mechanism, the Masked Language Model (MLM).

What Is BERT?

BERT (Bidirectional Encoder Representations from Transformers) is a natural language processing (NLP) model developed by Google that can understand sentences by simultaneously considering both the preceding and following context. Traditional language models could only capture either the forward or backward context, but BERT’s bidirectional nature allows for a deeper comprehension of meaning by leveraging the full context.

Understanding BERT Through an Analogy

BERT acts like a reader who simultaneously considers both the preceding and following context when reading a sentence. For example, in the sentence “He is jogging in the park,” BERT not only considers the relationship between “He” and “jogging” but also the connection between “park” and “jogging.” This bidirectional understanding supports BERT’s high performance in NLP tasks.

What Is the Masked Language Model (MLM)?

The Masked Language Model (MLM) is one of BERT’s training methods, where some words in a sentence are masked (hidden), and the model is tasked with predicting these hidden words. This approach enables the model to develop the ability to predict missing words based on the overall context.

How Masking Works

In BERT, about 15% of the words in the training data are masked. These masked words are typically replaced with a special token, “[MASK].” However, not all masked words are replaced with “[MASK]” but instead follow these three patterns:

1. 80% of the time, the word is replaced with “[MASK].”
2. 10% of the time, the word is replaced with a random word.
3. 10% of the time, the word remains unchanged.

By using this varied masking strategy, BERT enhances its flexibility in understanding context.

Example: How Masking Works

Consider the sentence, “I read a book at the [MASK].” BERT is trained to predict an appropriate word such as “library” or “cafe” based on the context. This training helps the model use bidirectional context to make accurate predictions.

Advantages of Learning with MLM

1. Bidirectional Context Understanding

The Masked Language Model allows BERT to understand both the preceding and following context simultaneously. This capability enables more accurate word predictions and improves overall sentence comprehension.

2. Robustness to Noisy Data

The varied masking technique trains BERT to make accurate predictions even when confronted with random words or masked words, enhancing its ability to handle noisy data effectively.

3. Improved Generalization

By learning from diverse sentence contexts, BERT acquires strong representations that can be applied to various NLP tasks. This allows BERT to perform exceptionally well in tasks such as classification, summarization, and machine translation.

BERT’s Training Steps

BERT’s training consists of two main steps:

1. Pre-training: The model is pre-trained on a large corpus of text using two tasks: the Masked Language Model (MLM) and Next Sentence Prediction (NSP).

• MLM: The model predicts masked words within a sentence.
• NSP: The model predicts whether two sentences are consecutive or not, learning the relationship between sentences.

1. Fine-tuning: The pre-trained model is then fine-tuned for specific tasks. For example, fine-tuning can be performed for tasks like question answering or sentiment analysis, adapting the model to the requirements of these tasks.

Practical Applications of BERT and the Masked Language Model

1. Machine Translation

BERT’s deep understanding of sentence meaning enhances the accuracy of machine translation, especially for long or complex sentences.

2. Text Summarization

In text summarization tasks, BERT provides high-precision summaries. By leveraging its bidirectional context comprehension, BERT effectively extracts essential information to create concise summaries.

3. Question-Answering Systems

BERT is also applied in question-answering systems. By understanding the context of both the question and possible answers simultaneously, BERT can provide accurate responses.

Summary

In this episode, we discussed BERT and the Masked Language Model. BERT utilizes bidirectional context understanding and the MLM technique to achieve high-precision predictions. This technology is expected to enhance the accuracy of various NLP tasks. In the next episode, we will cover evaluation metrics for text generation, such as perplexity and the BLEU score, to understand how the quality of generated text is assessed.

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Preview of the Next Episode

Next time, we will explain evaluation metrics for text generation. We will learn about methods like perplexity and the BLEU score, gaining insights into how to evaluate the quality of generated text. Stay tuned!

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Annotations

1. BERT (Bidirectional Encoder Representations from Transformers): A bidirectional Transformer model for NLP that understands both the preceding and following context simultaneously.
2. Masked Language Model (MLM): A task where certain words in a sentence are masked, and the model predicts them. It is a primary training method for BERT.
3. Next Sentence Prediction (NSP): A task where the model predicts whether two sentences are consecutive, learning the relationship between sentences.
4. Pre-training and Fine-tuning: A two-step learning process where the model acquires general knowledge during pre-training and is then fine-tuned for specific tasks.