BERT Explains: Next-Level Natural Language Processing
Analytics always incorporates the latest open source NLP development into our technology stack. Recently, a new transfer learning process called BERT (short for Bidirectional Encoder Representations for Transformers) has created huge waves in the NLP research space. Basically, BERT is very effective in dealing with what can be described as “very serious” language problems.
BERT NLP Briefly
Historically, Natural Language Processing (NLP) models strive to classify words based on context. For example:
He turned off the clock.
compared
Her mother’s mockery left a scar that had never healed.
Previously, textual analysis relied on shallow embedding methods. In this case, “embedding” is the process of mapping a different value (such as the word “wound”) into a continuous vector. In these traditional embedding methods, the given name can be assigned to only one vector. In other words, the vector of the “wound” needs to include details about the clocks and all things related to the injury. BERT is different; tries to mark vectors in words after reading the whole sentence
So How Does It Work?
BERT takes a completely different approach to learning. Basically, BERT is given billions of sentences during training. It is then asked to predict random selection of words that are not in these sentences. After practicing the text corpus several times, BERT received a good understanding of how a sentence is grammatically related. It is also better to predict ideas that may come together. This is how it works best in dealing with homonyms, such as “wound.
BERT Accelerates NLP Model Building
Modeling the language – even though it sounds scary – is actually a wordless prediction.
– Keita Kurita
Computational Data Science Post-Graduate
Carnegie Mellon University
BERT is open source, and all of this encrypted information is available upon use. This makes it a great value for construction models! It means you can gain state-of-the-art accuracy, or gain comparable accuracy in older algorithms, for a tenth of the amount of data.
To learn more about BERT (and ELMO, too!), Refer to Keita Kurita’s excellent article on Medium. If you would like to learn more about the in-depth study of Lexalytics and NLP in general, read our in-depth description of Natural Linguistics Analysis.
Categories: Machine Learning, Technology
Tags: bert, in-depth reading, machine learning, ML, natural language processing, NLP, technology
BERT Explained: The state of the NLP art language model
BERT (Bidirectional Encoder from Transformers) is the latest paper published by researchers in Google AI Language. It has caused a stir in the machine learning community by presenting high-quality results in a variety of NLP activities, including Answering a Question (SQuAD v1.1), Natural Language Inference (MNLI), and more.
BERT’s innovative innovation uses dual training of Transformer, a popular attention model, in language modeling. This is in contrast to previous attempts that looked at text sequence from left to right or combined left to right and training from right to left. The results of this paper suggest that a dual-language language model may have a deeper sense of language content and flow than single-minded genres. In the paper, researchers provide details about a novel process called Masked LM (MLM) that allows bidirectional training on models where previously it would not have been possible.
In the background
In the field of computer viewing, researchers have repeatedly demonstrated the value of learning transfers – before training a neural network model in a well-known work, for example Imagine, and then creating good programming – using a trained neural network as the basis for a new purpose-oriented model. In recent years, researchers have suggested that the same process might be useful for more natural language activities.
A different approach, also popular in NLP activities and demonstrated in the latest ELMo paper, feature-based training. In this way, a pre-trained neural network produces embedded words and is then used as elements in NLP models.
How BERT works
BERT uses Transformer, an attention-grabbing method that learns the contextual relationships between words (or small words) in a text. With its vanilla method, Transformer combines two distinct modes – an encoder that reads text input and a decoder that generates activity prediction. Since the purpose of BERT is to produce a language model, only the encoding method is required. The Transformer’s detailed functionality is described in the paper by Google.
In contrast to directing models, which read text input sequentially (from left to right or right-left), the Transformer encoder reads the word order simultaneously. It is therefore considered a double standard, or it may be more accurate to say that it is incorrect. This feature allows the model to read the context of a word based on the surrounding environment (left and right of the word).
The chart below is a description of the high-level Transformer encoder. Input is a sequence of tokens, which are initially embedded in vectors and then fixed to the neural network. The output is a sequence of H-size vectors, in which each vector corresponds to an input token with the same index.
When training language models, there is the challenge of defining the purpose of prediction. Many models predict the next word in sequence (e.g. “The child came home from _”), a directional method that naturally limits the reading of context. To overcome this challenge, BERT uses two training strategies:
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