Benchmarking Data-driven Automatic Text Simplification for G erman (2020)

Abstract: Automatic text simplification is an active research area, and there are first systems for English, Spanish, Portuguese, and Italian. For German, no data-driven approach exists to this date, due to a lack of training data. In this paper, we present a parallel corpus of news items in German with corresponding simplifications on two complexity levels. The simplifications have been produced according to a well-documented set of guidelines. We then report on experiments in automatically simplifying the German news items using state-of-the-art neural machine translation techniques. We demonstrate that despite our small parallel corpus, our neural models were able to learn essential features of simplified language, such as lexical substitutions, deletion of less relevant words and phrases, and sentence shortening.

## introduction simplified language is a variety of standard language characterized by reduced lexical and syntactic complexity, the addition of explanations for difficult concepts, and clearly structured layout. 1 among the target groups of simplified language are persons with cognitive impairment and learning disabilities, prelingually deaf persons, functionally illiterate persons, and foreign language learners @xcite . automatic text simplification, the process of automatically producing a simplified version of a standard-language text, was initiated in the late 1990s @xcite and since then has been approached by means of rule-based and statistical methods. as part of a rule-based approach, the operations carried out typically include replacing complex lexical and syntactic units by simpler ones. a statistical approach generally conceptualizes the simplification task as one of converting a standardlanguage into a simplified-language text using machine translation techniques. research on automatic text simplification has been documented for english @xcite , spanish @xcite , portuguese (aluisio and @xcite , french @xcite , and italian @xcite . to the authors' knowledge, the work of @xcite and @xcite , who presented a prototype of a rulebased text simplification system, is the only proposal for german. the paper at hand presents the first experiments in datadriven simplification for german, relying on neural machine translation. the data consists of news items manually simplified according to a well-known set of guidelines. hence, the contribution of the paper is twofold: ## conclusion this paper introduces the first parallel corpus for datadriven automatic text simplification for german. the corpus consists of 3,616 sentence pairs. since simplification of austria press agency news items is ongoing, the size of our corpus will increase continuously. a parallel corpus of the current size is generally not sufficient to train a neural machine translation system that produces both adequate and fluent text simplifications. however, we demonstrated that even with the limited amount of data available, our models were able to learn some essential features of simplified language.