Cognitive Simplification Operations Improve Text Simplification
This work addresses the problem of making text more accessible for people with cognitive disabilities, offering an incremental improvement by adapting existing methods to a new sub-task.
The paper tackled the sub-task of Cognitive Simplification (CS) in text simplification, which had not been explored with neural methods, by incorporating cognitive accessibility knowledge as an inductive bias into a model; the result was that this model adapted better to CS without CS data and outperformed a baseline on a traditional TS benchmark, while also providing a novel CS test dataset.
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.