Text Simplification (TS) is the task of converting a text into a form that is
easier to read while maintaining the meaning of the original text. A sub-task
of TS is Cognitive Simplification (CS), converting text to a form that is
readily understood by people with cognitive disabilities without rendering it
childish or simplistic. This sub-task has yet to be explored with neural
methods in NLP, and resources for it are scarcely available. In this paper, we
present a method for incorporating knowledge from the cognitive accessibility
domain into a TS model, by introducing an inductive bias regarding what
simplification operations to use. We show that by adding this inductive bias to
a TS-trained model, it is able to adapt better to CS without ever seeing CS
data, and outperform a baseline model on a traditional TS benchmark. In
addition, we provide a novel test dataset for CS, and analyze the differences
between CS corpora and existing TS corpora, in terms of how simplification
operations are applied.

本文中，我们呈现了一种将认知辅助领域的知识引入到文本简化模型中的方法，通过引入归纳偏差，决定何时使用简化操作。我们证明了通过将这种归纳偏差添加到文本简化模型中，它能够更好地适应认知简化而无需查看简化数据，并且在传统的文本简化基准测试上优于基线模型。此外，我们提供了一个新颖的认知简化测试数据集，并分析了认知简化语料库与现有文本简化语料库之间在应用简化操作方面的区别。