BERT (language model)

Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based machine learning technique for natural language processing (NLP) pre-training developed by Google. BERT was created and published in 2018 by Jacob Devlin and his colleagues from Google. In 2019, Google announced that it had begun leveraging BERT in its search engine, and by late 2020 it was using BERT in almost every English-language query. A 2020 literature survey concluded that "in a little over a year, BERT has become a ubiquitous baseline in NLP experiments", counting over 150 research publications analyzing and improving the model.

The original English-language BERT has two models: (1) the BERT_BASE: 12 encoders with 12 bidirectional self-attention heads, and (2) the BERT_LARGE: 24 encoders with 16 bidirectional self-attention heads. Both models are pre-trained from unlabeled data extracted from the BooksCorpus with 800M words and English Wikipedia with 2,500M words.

Architecture

BERT is at its core a transformer language model with a variable number of encoder layers and self-attention heads. The architecture is "almost identical" to the original transformer implementation in Vaswani et al. (2017).

BERT was pretrained on two tasks: ''language modeling'' (15% of tokens were masked and BERT was trained to predict them from context) and ''next sentence prediction'' (BERT was trained to predict if a chosen next sentence was probable or not given the first sentence). As a result of the training process, BERT learns contextual embeddings for words. After pretraining, which is computationally expensive, BERT can be finetuned with fewer resources on smaller datasets to optimize its performance on specific tasks.

Performance

When BERT was published, it achieved state-of-the-art performance on a number of natural language understanding tasks:

* GLUE ( General Language Understanding Evaluation) task set (consisting of 9 tasks)
* SQuAD (Stanford Question Answering Dataset) v1.1 and v2.0
* SWAG (Situations With Adversarial Generations)
* Sentiment Analysis: sentiment classifiers based on BERT achieved remarkable performance in several languages

Analysis

The reasons for BERT's state-of-the-art performance on these natural language understanding tasks are not yet well understood. Current research has focused on investigating the relationship behind BERT's output as a result of carefully chosen input sequences, analysis of internal vector representations through probing classifiers, and the relationships represented by attention weights.

History

BERT has its origins from pre-training contextual representations including semi-supervised sequence learning, generative pre-training, ELMo, and ULMFit. Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, where BERT takes into account the context for each occurrence of a given word. For instance, whereas the vector for "running" will have the same word2vec vector representation for both of its occurrences in the sentences "He is running a company" and "He is running a marathon", BERT will provide a contextualized embedding that will be different according to the sentence.

On October 25, 2019, Google Search announced that they had started applying BERT models for English language search queries within the US. On December 9, 2019, it was reported that BERT had been adopted by Google Search for over 70 languages. In October 2020, almost every single English-based query was processed by BERT.

Recognition

The research paper describing BERT won the Best Long Paper Award at the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).

See also

* Transformer (machine learning model)
* Word2vec
* Autoencoder
* Document-term matrix
* Feature extraction
* Feature learning
* Neural network language models
* Vector space model
* Thought vector
* fastText
* GloVe
* TensorFlow

References

Further reading

*

External links

Official GitHub repository

BERT on Devopedia

{{Differentiable computing

Natural language processing
Computational linguistics
Speech recognition
Computational fields of study