论文标题
认知简化操作改善文本简化
Cognitive Simplification Operations Improve Text Simplification
论文作者
论文摘要
文本简化（TS）是将文本转换为在保持原始文本含义时更易于阅读的形式的任务。 TS的子任务是认知简化（CS），将文本转换为一种形式，该形式很容易被认知障碍的人理解，而不会使其幼稚或简单化。 NLP中的神经方法尚未探索此子任务，并且几乎无法获得资源。在本文中，我们提出了一种将认知可访问性域知识纳入TS模型的方法，通过引入有关使用哪些简化操作的电感偏差。我们表明，通过将这种归纳偏置添加到TS训练的模型中，它可以更好地适应CS，而无需看到CS数据，并且在传统TS基准上胜过基线模型。此外，我们还为CS提供了一种新颖的测试数据集，并在如何应用简化操作方面分析了CS Corpora和现有TS Corpora之间的差异。
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.
