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%2C encodes the input once and decodes the output in parallel%2C boosting both training and inference efficiency by avoiding duplicate input encoding and increasing the operational intensity (ratio of numbers of arithmetic operation to memory access) of decoding process by sharing the input key-value cache. We achieve computation reduction that roughly scales with the number of subtasks%2C gaining up to 4.6x speed-up over state-of-the-art models for dialogue state tracking%2C summarization%2C and question-answering tasks%2C with comparable or better performance.?quality=80&w=800)
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