Sequence-to-sequence learning is a machine learning technique where an input sequence is transformed into an output sequence, often used in tasks like language translation, text summarization, and dialogue generation. This approach is particularly effective for chatbots and virtual assistants because it allows for processing variable-length input and generating variable-length output, making interactions more natural and coherent. By utilizing models like recurrent neural networks (RNNs) or transformers, this technique captures the contextual relationships in the data to produce meaningful responses.
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