  Controllable text generation concerns two fundamental tasks of wide
applications, namely generating text of given attributes (i.e.,
attribute-conditional generation), and minimally editing existing text to
possess desired attributes (i.e., text attribute transfer). Extensive prior
work has largely studied the two problems separately, and developed different
conditional models which, however, are prone to producing biased text (e.g.,
various gender stereotypes). This paper proposes to formulate controllable text
generation from a principled causal perspective which models the two tasks with
a unified framework. A direct advantage of the causal formulation is the use of
rich causality tools to mitigate generation biases and improve control. We
treat the two tasks as interventional and counterfactual causal inference based
on a structural causal model, respectively. We then apply the framework to the
challenging practical setting where confounding factors (that induce spurious
correlations) are observable only on a small fraction of data. Experiments show
significant superiority of the causal approach over previous conditional models
for improved control accuracy and reduced bias.
