  While diffusion models have achieved great success in generating continuous
signals such as images and audio, it remains elusive for diffusion models in
learning discrete sequence data like natural languages. Although recent
advances circumvent this challenge of discreteness by embedding discrete tokens
as continuous surrogates, they still fall short of satisfactory generation
quality. To understand this, we first dive deep into the denoised training
protocol of diffusion-based sequence generative models and determine their
three severe problems, i.e., 1) failing to learn, 2) lack of scalability, and
3) neglecting source conditions. We argue that these problems can be boiled
down to the pitfall of the not completely eliminated discreteness in the
embedding space, and the scale of noises is decisive herein. In this paper, we
introduce DINOISER to facilitate diffusion models for sequence generation by
manipulating noises. We propose to adaptively determine the range of sampled
noise scales for counter-discreteness training; and encourage the proposed
diffused sequence learner to leverage source conditions with amplified noise
scales during inference. Experiments show that DINOISER enables consistent
improvement over the baselines of previous diffusion-based sequence generative
models on several conditional sequence modeling benchmarks thanks to both
effective training and inference strategies. Analyses further verify that
DINOISER can make better use of source conditions to govern its generative
process.
