What are the two main steps of the attention mechanism?Calculating the attention weights and generating the output wordCalculating the context vector and generating the attention weightsCalculating the attention weights and generating the context vectorCalculating the context vector and generating the output word

What is the attention mechanism?A way of determining the similarity between two sentencesA way of determining the importance of each word in a sentence for the translation of another sentenceA way of predicting the next word in a sentenceA way of identifying the topic of a sentence

What is the purpose of the attention weights?To generate the output word based on the input data alone.To assign weights to different parts of the input sequence, with the most important parts receiving the highest weights.To incrementally apply noise to the input data.To calculate the context vector by averaging words embedding in the context.

How is the final attention output computed using the attention weights and value vectors?<br /> A. a. By taking the dot product of the attention weights and value vectors <br />B. b. By concatenating the attention weights and value vectors <br />C. c. By taking a weighted sum of the value vectors using the attention weights <br />D. d. By adding the attention weights to the value vectors element-wise

What is the primary function of the self-attention mechanism in transformers?Group of answer choicesTo perform backpropagationTo reduce the computational costTo reduce the computational cost of trainingTo allow the model to weigh the importance of different words in a sentence relative to each other

What is the purpose of the attention mechanism in an encoder-decoder model?To translate text from one language to another.To extract information from the image.To allow the decoder to focus on specific parts of the image when generating text captions.To generate text captions for the image.