Neural Machine Translation Components Overview

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What is the purpose of the self-attention layer in the encoding component?

To determine the length of the longest sentence in the training dataset

To connect the encoder and decoder components

To help the encoder look at other words in the input sentence as it encodes a specific word (correct)

To calculate the word embeddings directly

What is the primary function of the embedding layer in a Transformer model?

Capture the meaning of each word or token in a vector space (correct)

Model complex relationships between input tokens

Perform self-attention calculations

Apply non-linear transformations to the input sequence

Where does the embedding algorithm operate in the encoder-decoder model described?

In the decoder's attention layer

Only in the decoder layers

In the bottom-most encoder (correct)

It operates after the self-attention layer

In the Transformer architecture, what are the two sub-layers present in each encoder or decoder layer?

Self-attention mechanism and feedforward neural network Signup and view all the answers

How does the multi-head attention mechanism in Transformers handle attending to different parts of the input sequence simultaneously?

By applying multiple self-attention mechanisms in parallel Signup and view all the answers

What is the purpose of the attention layer between the decoder's self-attention and feed-forward layers?

To help the decoder focus on relevant parts of the input sentence Signup and view all the answers

What is common to all the encoders described in the text?

They receive a list of vectors, each of size 512 Signup and view all the answers

What is the purpose of the feedforward neural network component in the Transformer architecture?

Applying non-linear transformations to the input Signup and view all the answers

How does the self-attention mechanism in Transformers allow the model to focus on different parts of the input sequence?

By learning and calculating attention weights for each position Signup and view all the answers

How does each word in the input sequence flow through an encoder?

Each word flows through each of the two layers of the encoder Signup and view all the answers

Which component of Transformer models helps in capturing the semantic meaning of individual words or tokens?

Embedding Layer Signup and view all the answers

What determines the length of the list of vectors received by each encoder?

The length of the longest sentence in the training dataset Signup and view all the answers

What is the purpose of the Output layer in the described model architecture?

Converting the encoded representation into word probabilities Signup and view all the answers

What role does the Decoder stack play in the processing of the target sequence?

It processes the encoded representation from the Encoder stack Signup and view all the answers

How does a pre-trained model benefit downstream NLP tasks?

By fine-tuning on a specific downstream task Signup and view all the answers

In the described model architecture, what happens after taking the last word of the output sequence as the predicted word?

The word is filled into the second position of the Decoder input sequence Signup and view all the answers

What is the primary purpose of training a model on a general task before fine-tuning it on a specific downstream task?

To learn general language representations Signup and view all the answers

Why is it unnecessary to repeat steps #1 and #2 for each iteration in the described model architecture?

Because the Encoder sequence remains unchanged Signup and view all the answers

Neural Machine Translation Components Overview

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