A word in a sentence derives its meaning from the context from the words preceding it (left context) and those that come after it (right context). One key characteristic of all NLU tasks is that the input data is sequential in nature. Transformers were introduced in the seminal paper “Attention Is All You Need,” which set the stage for rapid advancements in the field of natural language understanding (NLU). In this article I will share how the state of the art natural language models that are based on transformers came to be and explain the core innovation around its architecture: the a ttention layer.īut before that, let us briefly touch upon the nature of the problem and the history of neural networks used in this field. 2017 marked the release of Michael Bay's fifth and last movie on sentient shape-shifting vehicles from outer space, but in the same year Google Brain did one better with transformers: it introduced a ground breaking neural network architecture called just that.
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