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.

本文介绍了 CEV-LM，一种轻量级半自回归语言模型，利用约束编辑向量来控制文本的速度、音量和曲折度这三个衡量文本形状的指标，研究表明 CEV-LM 能够更精确地控制这三个指标，同时保持语义内容、使用更少的训练数据和参数。