  As large-scale language models become the standard for text generation, there
is a greater need to tailor the generations to be more or less concise,
targeted, and informative, depending on the audience/application. Existing
control approaches primarily adjust the semantic (e.g., emotion, topics),
structural (e.g., syntax tree, parts-of-speech), and lexical (e.g.,
keyword/phrase inclusion) properties of text, but are insufficient to
accomplish complex objectives such as pacing which control the complexity and
readability of the text. In this paper, we introduce CEV-LM - a lightweight,
semi-autoregressive language model that utilizes constrained edit vectors to
control three complementary metrics (speed, volume, and circuitousness) that
quantify the shape of text (e.g., pacing of content). We study an extensive set
of state-of-the-art CTG models and find that CEV-LM provides significantly more
targeted and precise control of these three metrics while preserving semantic
content, using less training data, and containing fewer parameters.
