Cognitive Simplification Operations Improve Text Simplification

TL;DR

This paper introduces a method to incorporate cognitive accessibility knowledge into text simplification models, enabling better adaptation to cognitive simplification tasks without requiring specific training data.

Contribution

It presents a novel approach using an inductive bias for cognitive simplification, along with a new dataset and analysis of simplification operations in CS versus TS.

Findings

Model with inductive bias outperforms baseline on TS benchmark

Method adapts to CS without CS training data

Provides a new dataset for cognitive simplification

Abstract

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.