  Multi-aspect controllable text generation aims to generate fluent sentences
that possess multiple desired attributes simultaneously. Traditional methods
either combine many operators in the decoding stage, often with costly
iteration or search in the discrete text space, or train separate controllers
for each aspect, resulting in a degeneration of text quality due to the
discrepancy between different aspects. To address these limitations, we
introduce a novel approach for multi-aspect control, namely MacLaSa, that
estimates compact latent space for multiple aspects and performs efficient
sampling with a robust sampler based on ordinary differential equations (ODEs).
To eliminate the domain gaps between different aspects, we utilize a
Variational Autoencoder (VAE) network to map text sequences from varying data
sources into close latent representations. The estimated latent space enables
the formulation of joint energy-based models (EBMs) and the plugging in of
arbitrary attribute discriminators to achieve multi-aspect control. Afterwards,
we draw latent vector samples with an ODE-based sampler and feed sampled
examples to the VAE decoder to produce target text sequences. Experimental
results demonstrate that MacLaSa outperforms several strong baselines on
attribute relevance and textual quality while maintaining a high inference
speed.
