All You Need to know about BERT

This article was published as a part of the Data Science Blogathon


Machines understand language through language representations. These language representations are in the form of vectors of real numbers. Proper language representation is necessary for a better understanding of the language by the machine. Language representations are of two types: (i) Context-free language representation such as Glove and Word2vec where embeddings for each token in the vocabulary are constant and it doesn’t depend on the context of the word. (ii) Contextual language representation such as Elmo and OpenAI-GPT where token representation depends on the context of the word where it is used.

The problem with context-free language representation is that the same word with completely different meanings will have the same representation such as the representation of ‘bat’ will be the same for cricket bat or animal. With a contextual model, we get the representation of each token based on the sentence in which that token is used resulting in a better understanding




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