Free Printable NLP_8

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NLP_8_RNN

Total questions: 10

Worksheet time: 7mins

Name

Class

Date

What is the purpose of language models in NLP?

To determine the probability of a sequence of words

b) To translate text from one language to another

c) To generate semantic embeddings

d) To generate random sentences

e) To predict the next word given a sequence of words

What is the main limitation of bag of words techniques in encoding document vectors?

a) They cannot handle large datasets

b) They do not consider word order

c) They require a lot of computational power

d) They are always less accurate than other techniques

e) They are usually very sparse

What is the intuition behind recurrence networks?

a) The output is a function of the current input and the previous state

b) The output is a function of the current input only

c) The output is a function of the previous state only

d) The output is a random function

What is the main benefit of using static word embeddings in language models?

a) To improve the accuracy of language models

b) To reduce the computational complexity of language models

c) To handle large datasets more efficiently

d) To generate random sentences

e) Capture the contextual semantics

What is the primary advantage of using Recurrent Neural Networks (RNNs) over traditional feed-forward neural networks in natural language processing?

a) RNNs have a simpler architecture

b) RNNs can process variable-length sequences

c) RNNs require less training data

d) RNNs are computationally less expensive

RNNs are usually more accurate than FFNN

In the context of an RNN cell, what does the term 'recurrence' refer to?

a) Repeated application of the same function

b) Feedback loop from output back to input

c) Regular updating of network weights

d) Reuse of the same layers for each input

e) Feedback from the hidden state back to the input

In RNNs, what is the key significance of the hidden state (h_t) at each time step?

a) It holds the output of the network at that step

b) It is the input for the next layer of the network

c) It represents the 'memory' of the network up to that point

d) It is used to calculate the backpropagation error

What are the possible causes of the vanishing gradient problem in traditional RNNs?

a) The repeated multiplication of gradients through the network layers

b) The use of non-linear activation functions like sigmoid or tanh

c) The limited memory capacity of RNNs

d) The use of large learning rates during training

e) The use of ReLU activation

Which of the following is NOT True about RNNs

The output of the RNN is always the same as h(t)

The dimension of the weight matrix depends of the shape of the input x(t)

The main activation function is either ReLU or tanh

RNNs are designed to handle sequential data

The hidden state accumulates memory from previous inputs

10.

Check the wrong statement :

Training an RNN on very long sequences may easily result in vanishing Gradient problem

In sequence labeling task, at each time step, the predicted probability distribution over the vocabulary is created using a softmax activation applied to the hidden layer output.

In Language Modeling Task, the loss function at a given time step is calculated as the negative log probability of the correct target word in the vocabulary.

The hidden state at the first time step is initialized to the embedding of the first word in the training sequence.

In RNN Language Modeling Task, the objective is to maximize the probability of the correct word at the current time step, given the context seen so far.

100 %

NLP_8_RNN

Total questions: 10

Worksheet time: 7mins

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