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Unaligned Data in Sequence Learning
In sequence-to-sequence learning tasks like machine translation, models often must handle unaligned data, where the input and output sequences do not share a strict one-to-one correspondence. Unaligned data introduces two primary challenges: the input and output sequences may have entirely different lengths, and the corresponding regions of meaning may appear in a different order, as seen when translating sentences between languages with different grammatical structures.
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