Given the sequence of words in a sentence, which of the following RNN variations would be best suited for understanding long-term dependencies in that sequence, especially when the context of earlier words must be maintained for later words?

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Seekh · Accepted Answer

For sentences where a word far earlier can influence a word much later, a vanilla RNN struggles because the signal fades as the sequence grows. A Long Short‑Term Memory network, or LSTM, is designed to keep a memory cell that can store information over many steps, updating it with gates that decide what to keep or forget. These gates let the model remember a word from the start of the sentence and still use it when predicting a word at the end. For example, in “The cat that chased the mouse was tired,” an LSTM can remember “cat” when deciding the adjective “tired” refers to the cat, not the mouse. Thus, LSTMs are the preferred choice for long‑term dependencies in language.

Given the sequence of words in a sentence, which of the following RNN variations would be best suited for understanding long-term dependencies in that sequence, especially when the context of earlier words must be maintained for later words?

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