What is the primary reason the Transformer model has significantly improved machine translation tasks compared to previous models?

The Transformer improves translation mainly because it uses self‑attention, letting every word look at every other word at once, so long‑range relationships are captured without the slow step‑by‑step processing of RNNs. This means the model can handle very long sentences in parallel, speeding up training and inference. Self‑attention also gives the network a clear view of how each word relates to all others, which is crucial for translating idiomatic phrases. For example, translating “The cat sits on the mat” to Spanish requires understanding that “cat” and “mat” are linked; self‑attention lets the model link them directly. As a result, Transformers achieve higher accuracy and faster translation than earlier recurrent models.