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
Question
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
Solution
The correct answer is b and d.
b. For long sentences, it gives good accuracy and is computationally expensive: This is true. RNNs, LSTMs, and GRUs are all capable of handling long sequences, which is beneficial for tasks like machine translation. However, this comes at the cost of computational expense, especially for very long sequences.
d. Vanishing gradient problem: This is also true. RNNs are particularly susceptible to the vanishing gradient problem, which can make them difficult to train effectively. LSTMs and GRUs were designed to mitigate this issue, but they are not completely immune to it.
Option a is incorrect because options b and d are correct. Option c is incorrect because while these models can handle large input sequences, it is not a disadvantage.
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