BERT is an open source machine learning framework for natural language processing (NLP) that helps computers understand ambiguous language by using context from surrounding text. The model was pretrained on text from English Wikipedia and the Brown Corpus, and it can be fine-tuned with question-and-answer datasets. BERT stands for Bidirectional Encoder Representations from Transformers and is based on the transformer model, which connects every output element to every input element dynamically.
What is BERT?
BERT is designed to tackle the challenges of interpreting natural language. Traditional language models processed text only sequentially—from left-to-right or right-to-left—which made context understanding limited. BERT overcomes this limitation by reading text bidirectionally, allowing it to capture the full context of words. Its pretraining on vast, unlabeled text and ability to be fine-tuned on specific tasks make it a powerful tool in modern NLP.
How BERT works
BERT’s strength comes from its underlying transformer architecture. Unlike earlier models that depended on fixed sequences, transformers process data in any order, which lets BERT weigh the influences of all words around a target word. This bidirectional approach sharpens its understanding of language.
Transformer architecture
The transformer model forms the backbone of BERT. It ensures that each output element is dynamically calculated based on every input element. This design enables BERT to handle context by examining relationships across the entire sentence, not just in a one-way progression.
Masked language modeling
BERT uses a technique called masked language modeling (MLM). In MLM, certain words in a sentence are hidden, and BERT must predict these masked words based on the rest of the sentence. This forces the model to develop a deep understanding of context rather than relying on static word representations.
Next sentence prediction
Next sentence prediction (NSP) trains BERT to determine whether one sentence logically follows another. By learning the relationship between sentence pairs—both correctly and incorrectly paired—BERT improves its ability to capture the flow of language, which is crucial for tasks like question answering.
Self-attention mechanisms
Self-attention allows BERT to weigh each word’s relevance relative to others in a sentence. This mechanism is key when a word’s meaning shifts as new context is added, ensuring that BERT’s interpretation remains accurate even when words are ambiguous.
Background and history
The development of BERT marked a significant departure from earlier language models. Prior models, such as those based on recurrent neural networks (RNN) and convolutional neural networks (CNN), processed text in a fixed, sequential order. This limitation hindered their performance on tasks that relied on understanding context fully.
In 2017, Google introduced the transformer model, paving the way for innovations like BERT. By 2018, Google released and open-sourced BERT, and research showed that it achieved state-of-the-art results on 11 natural language understanding tasks, including sentiment analysis, semantic role labeling, and text classification. In October 2019, Google applied BERT to its U.S.-based search algorithms, enhancing the understanding of roughly 10% of English search queries. By December 2019, BERT had been extended to over 70 languages, improving both voice and text-based search.
Applications and uses
BERT has a wide range of applications in NLP, enabling both general-purpose and specialized tasks. Its design makes it ideal for improving the accuracy of language understanding and processing.
NLP tasks
BERT supports sequence-to-sequence tasks like question answering, abstract summarization, sentence prediction, and conversational response generation. It also excels at natural language understanding tasks such as word sense disambiguation, polysemy resolution, natural language inference, and sentiment classification.
Specialized variants
Many adaptations of BERT have been developed to optimize performance or target specific domains. Examples include PatentBERT for patent classification, DocBERT for document classification, BioBERT for biomedical text mining, and SciBERT for scientific literature. Other versions like TinyBERT, DistilBERT, ALBERT, SpanBERT, RoBERTa, and ELECTRA offer improvements in speed, efficiency, or task-specific accuracy.
BERT vs. GPT
While both BERT and GPT are top language models, they serve different purposes. BERT focuses on understanding text by reading it in its entirety using context from both directions. This makes it ideal for tasks like search query interpretation and sentiment analysis. In contrast, GPT models are designed for text generation, excelling at creating original content and summarizing lengthy texts.
Impact on AI and search
Google uses BERT to enhance the interpretation of search queries by understanding context better than previous models. This has led to more relevant results for about 10% of U.S. English search queries. BERT’s ability to process context has also improved voice search and text-based search accuracy, particularly because it has been adapted for use in over 70 languages. Its influence extends throughout AI, setting new standards for natural language understanding and pushing the development of more advanced models.
