NPTEL Deep Learning - IIT Ropar Week 9 Assignment Answers 2024

NPTEL Deep Learning – IIT Ropar Week 9 Assignment Answers 2024

1. Consider the following corpus: “human machine interface for computer applications. user opinion of computer system response time. user interface management system. system engineering for improved response time”. What is the size of the vocabulary of the above corpus?

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2. At the input layer of a continuous bag of words model, we multiply a one-hot vector x∈R|V|
with the parameter matrix W∈R^k×|V|. What does each column of W correspond to?

the representation of the i-th word in the vocabulary
the i-th eigen vector of the co-occurrence matrix

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3. Suppose that we use the continuous bag of words (CBOW) model to find vector representations of words. Suppose further that we use a context window of size 3 (that is, given the 3 context words, predict the target word P(w_t|(w_i,w_j,w_k))). The size of word vectors (vector representation of words) is chosen to be 100 and the vocabulary contains 10,000 words. The input to the network is the one-hot encoding (also called 1-of-V encoding) of word(s). How many parameters (weights), excluding bias, are there in W_word
? Enter the answer in thousands. For example, if your answer is 50,000, then just enter 50.

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5. Consider a skip-gram model trained using hierarchical softmax for analyzing scientific literature. We observe that the word embeddings for ‘Neuron’ and ‘Brain’ are highly similar. Similarly, the embeddings for ‘Synapse’ and ‘Brain’ also show high similarity. Which of the following statements can be inferred?

‘Neuron’ and ‘Brain’ frequently appear in similar contexts
The model’s learned representations will indicate a high similarity between ‘Neuron’ and ‘Synapse’
The model’s learned representations will not show a high similarity between ‘Neuron’ and ‘Synapse’
According to the model’s learned representations, ‘Neuron’ and ‘Brain’ have a low cosine similarity

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6. Which of the following is an advantage of the CBOW model compared to the Skip-gram model?

It is faster to train
It requires less memory
It performs better on rare words
All of the above

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7. Which of the following is true about the input representation in the CBOW model?

Each word is represented as a one-hot vector
Each word is represented as a continuous vector
Each word is represented as a sequence of one-hot vectors
Each word is represented as a sequence of continuous vectors

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8. Which of the following is an advantage of using the skip-gram method over the bag-of-words approach?

The skip-gram method is faster to train
The skip-gram method performs better on rare words
The bag-of-words approach is more accurate
The bag-of-words approach is better for short texts

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9. What is the role of the softmax function in the skip-gram method?

To calculate the dot product between the target word and the context words
To transform the dot product into a probability distribution
To calculate the distance between the target word and the context words
To adjust the weights of the neural network during training

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10. How does Hierarchical Softmax reduce the computational complexity of computing the softmax function?

It replaces the softmax function with a linear function
It uses a binary tree to approximate the softmax function
It uses a heuristic to compute the softmax function faster
It does not reduce the computational complexity of computing the softmax function