FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

EMNLP/IJCNLP (1), pp. 4281-4291, 2019.

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We propose FlowSeq, an efficient and effective model for non-autoregressive sequence generation by using generative flows

Abstract:

Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the j...More

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