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?

The best choice is a Long Short‑Term Memory network, or LSTM. Its memory cell keeps a running record of earlier words, and the gating mechanisms decide when to remember or forget that information, which lets later words still “see” distant context. In a sentence, this means a word like “although” can influence the meaning of a word many tokens later, something a plain RNN would forget. For instance, in “The boy who cried wolf was eventually believed,” an LSTM can remember the subject “boy” when processing the later verb “believed. ” This ability to preserve useful information over long distances makes LSTMs ideal for long‑term dependency problems.