In a natural language processing application, a model is tasked with translating a sentence from English to French. The model uses an attention mechanism to focus on different words in the input sentence while generating each word of the output. Which of the following best explains how attention contributes to this translation process?

Attention lets the decoder look back at the input sentence each time it writes a word, so it can focus on the most relevant English words for that French word. It does this by computing a weighted sum of the encoder’s hidden states, giving higher weight to the parts of the sentence that matter most. For example, when translating “I eat apples,” the attention weight for the English word “apples” becomes high when the decoder generates the French word “pommes. ” This dynamic focusing lets the model handle long sentences and word order differences more accurately. As a result, the translation is more fluent and faithful to the source.