PARALLEL DECODING USING AUTOREGRESSIVE MACHINE LEARNING MODELS

    公开(公告)号:US20190354812A1

    公开(公告)日:2019-11-21

    申请号:US16417190

    申请日:2019-05-20

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing parallel generation of output from an autoregressive sequence to sequence model. In one aspect, a blockwise parallel decoding method takes advantage of the fact that some architectures can score sequences in sublinear time. By generating predictions for multiple time steps at once then backing off to a longest prefix validated by the scoring model, the methods can substantially improve the speed of greedy decoding without compromising performance.

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