Since computers don’t understand words-its meanings and its relationship between words like we do we need to replace the words with vectors. Let’s dive deeper and examine each component. This is the high-level details of how Transformer works. We repeat this process to generate the next word until the “end of sentence” token is generated. These vectors are passed into the feed-forward layer linear layer and the softmax layer to predict the next French word. Attention vectors from the Encoder and Decoder are fed into the next attention block, which generates attention mapping vectors for every English and French word. The masked attention block computes the Attention vectors for current and prior words. These word vectors are fed into the first attention block, the masked attention block. Then positional vectors are added to represent the context of the word in the sentence. It encodes each word’s meaning with the embedding layer. The Decoder receives input of the French word(s) and attention vectors of the entire English sentence, to generate the next French word. These attention vectors are passed in through a feed-forward network in parallel, and the output will be a set of encoded vectors for every word. These word vectors are fed into Encoder attention block, which computes the attention vectors for every word. Then we add a positional vector to add the context of the word in the sentence. Each word in the input English sentence is converted into an embedding to represent meaning. In the Encoder, it inputs an English sentence, and the output will be a set of encoded vectors for every word. These are also known as the long-term dependency problems in RNNs. Generally, you will see NaN (Not a Number) in the loss during the training process. Secondly, RNNs also can’t deal with long sequences very well as we get vanishing and exploding gradients if the input sequence is too long. Even with truncated backpropagation, RNNs are still slow to train.
WHAT DOES TRANSFORMER EN MEAN IN FRENCH UPDATE
RNNs are so slow that truncated backpropagation was introduced to limit the number of timesteps in the backward pass–estimating gradients to update the weights rather than backpropagation fully. Such a recurrent process does not make use of modern graphics processing units (GPUs), which were designed for parallel computation.
The input data needs to be processed sequentially one after the other. However, RNN models have some problems, they are slow to train, and they can’t deal with long sequences. Then, the Decode will take the fixed-length vector representation as input, and produce each French word one after another, forming the translated English sentence. The Encoder will unroll each word in sequence and forms a fixed-length vector representation of the input English sentence. RNNs work like a feed-forward neural network that unrolls the input over its sequence, one after another.įor example, in machine translation, the input is an English sentence, and the output is the French translation. Besides significant improvements in language translation, it has provided a new architecture to solve many other tasks, such as text summarization, image captioning, and speech recognition.īefore the Transformer model, recurrent neural networks (RNNs) have been the go-to method for sequential data, where the input data has a defined order. It has produced state-of-the-art performance in machine translation. Transformer relies entirely on Attention mechanisms to boost its speed by being parallelizable. Transformer based models have primarily replaced LSTM, and it has been proved to be superior in quality for many sequence-to-sequence problems. While encoder-decoder architecture has been relying on recurrent neural networks (RNNs) to extract sequential information, the Transformer doesn’t use RNN. The Transformer model is the evolution of the encoder-decoder architecture, proposed in the paper Attention is All You Need.