custom ai droid english story translation

In every neural network, information flows through so-called hidden units. When the network is being trained, the translation is always available, and the computation for the English words also can be done simultaneously. The strength of the green lines indicates how much the network focuses on each French word. There are two so-called layers in the decoder, and the animation illustrates how the attention is done once for each layer. We first run the encoder to create a vector for each French word using a CNN, and the computation is done simultaneously.Īt every step, the attention glimpses the French sentence to decide which words are most relevant to predict the next English word in the translation.

Multi-hop attention is an enhanced version of this mechanism, which allows the network to make multiple such glimpses to produce better translations.įor example, the first glimpse could focus on a verb and the second glimpse on the associated auxiliary verb. Much research has focused on speeding up neural networks through quantizing weights or distillation, to name a few methods, and those can be equally applied to the CNN model to increase speed even more, suggesting significant future potential.Īn attention mechanism is similar to the way a person would break down a sentence when translating it: Instead of looking at the sentence only once and then writing down the full translation without looking back, the network takes repeated glimpses at the sentence to choose which words it will translate next, much like a human occasionally looks back at specific keywords when writing down a translation. The FAIR CNN model is computationally very efficient and is nine times faster than strong RNN systems. On WMT 2014 English-German, the improvement is 0.5 BLEU, and on WMT 2016 English-Romanian, we improve by 1.8 BLEU. When the CNN and the best RNN of similar size are trained in the same way, the CNN outperforms it by 1.5 BLEU on the WMT 2014 English-French task, a widely used metric for judging the accuracy of machine translation. Nevertheless, because of the architectural potential of CNNs, FAIR began research that has led to a translation model design showing strong performance of CNNs for translation.

In comparison, CNNs can compute all elements simultaneously, taking full advantage of GPU parallelism.Īnother advantage of CNNs is that information is processed hierarchically, which makes it easier to capture complex relationships in the data. The computation cannot be fully parallelized, because each word must wait until the network is done with the previous word.

This is a less natural fit to the highly parallel GPU hardware that powers modern machine learning.

Custom ai droid english story translation