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        <title>Benchmarking Data-driven Automatic Text Simplification for German</title>
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        <namePart type="given">Andreas</namePart>
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            <title>Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)</title>
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            <namePart type="given">Núria</namePart>
            <namePart type="family">Gala</namePart>
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            <namePart type="given">Rodrigo</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Benchmarking Data-driven Automatic Text Simplification for German
%A Säuberli, Andreas
%A Ebling, Sarah
%A Volk, Martin
%Y Gala, Núria
%Y Wilkens, Rodrigo
%S Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-45-0
%G English
%F sauberli-etal-2020-benchmarking
%X 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.
%U https://aclanthology.org/2020.readi-1.7
%P 41-48

Markdown (Informal)

[Benchmarking Data-driven Automatic Text Simplification for German](https://aclanthology.org/2020.readi-1.7) (Säuberli et al., READI 2020)

• Benchmarking Data-driven Automatic Text Simplification for German (Säuberli et al., READI 2020)

ACL

• Andreas Säuberli, Sarah Ebling, and Martin Volk. 2020. Benchmarking Data-driven Automatic Text Simplification for German. In Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI), pages 41–48, Marseille, France. European Language Resources Association.