In the context of natural language processing, how are RNNs typically utilized for machine translation?As a replacement for CNNsEncoding the input sequence and decoding the output sequenceAs discriminators in GANsFor image classificationFor clustering text data

what are the advantages of using transformer networks over RNNs in the field of natural language processing with deep learning?

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

For neural machine translation we typically use encoder decoder architecture. Which of the following statements are correct regarding the disadvantages of RNN/LSTM/GRU variants of encoder decoder architecture?Question 3Answera.None of theseb.For long sentence it give good accuracy and computationally expensivec.Input sequence can be larged.Vanishing gradient problem

In NLP, what does RNNs help to predict?Next song noteNext imageNext video frameNext wordNone of the options given

Which of the following is a common architecture used for sequence learning in deep learning? Convolutional Neural Networks (CNNs) Autoencoders Recurrent Neural Networks (RNNs) Generative Adversarial Networks (GANs)