An analysis of crowdsourced text simplifications

Abstract

We present a study on the text simplification operations undertaken collaboratively by Simple English Wikipedia contributors. The aim is to understand whether a complex-simple parallel corpus involving this version of Wikipedia is appropriate as data source to induce simplification rules, and whether we can automatically categorise the different operations performed by humans. A subset of the corpus was first manually analysed to identify its transformation operations. We then built machine learning models to attempt to automatically classify segments based on such transformations. This classification could be used, e.g., to filter out potentially noisy transformations. Our results show that the most common transformation operations performed by humans are paraphrasing (39.80%) and drop of information (26.76%), which are some of the most difficult operations to generalise from data. They are also the most difficult operations to identify automatically, with the lowest overall classifier accuracy among all operations (73% and 59%, respectively).