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• W3042443178 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." @default.
• W3042443178 created "2020-07-23" @default.
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• W3042443178 date "2020-05-01" @default.
• W3042443178 modified "2023-09-27" @default.
• W3042443178 title "Benchmarking Data-driven Automatic Text Simplification for German" @default.
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• W3042443178 doi "https://doi.org/10.5167/uzh-188569" @default.
• W3042443178 hasPublicationYear "2020" @default.
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