Transformers in NLP: Creating a Translator Model from Scratch | Lionbridge AI - 0 views
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otakuhacks on 29 Sep 20Transformers have now become the defacto standard for NLP tasks. Originally developed for sequence transduction processes such as speech recognition, translation, and text to speech, transformers work by using convolutional neural networks together with attention models, making them much more efficient than previous architectures. And although transformers were developed for NLP, they've also been implemented in the fields of computer vision and music generation. However, for all their wide and varied uses, transformers are still very difficult to understand, which is why I wrote a detailed post describing how they work on a basic level. It covers the encoder and decoder architecture, and the whole dataflow through the different pieces of the neural network. In this post, we'll get deeper into looking at transformers by implementing our own English to German language translator.
