DiffuSeq adapts diffusion models to conditional sequence-to-sequence text generation and reports performance matching or exceeding strong baselines including pretrained language model systems while generating more diverse outputs.
(19) To make a better analogy to AR and NAR models, we use a lossless way to formulate iterative NAR models (Gu et al., 2019; Ghazvininejad et al
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DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
DiffuSeq adapts diffusion models to conditional sequence-to-sequence text generation and reports performance matching or exceeding strong baselines including pretrained language model systems while generating more diverse outputs.