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This paper introduces the first comprehensive dataset for Romanian that includes both lexical complexity prediction (LCP) annotations and lexical simplification (LS), enabling a deeper understanding of language simplification in this context. The authors employ a pairwise ranking approximation method to order simplification suggestions based on human judgments, facilitating the development of a text simplification system tailored for Romanian. Key findings reveal effective pipelines for complexity prediction and simplification, marking a significant advancement in Romanian natural language processing resources.
The first dataset for Romanian that bridges lexical complexity prediction and simplification reveals new insights into effective text simplification strategies.
We introduce the first dataset that jointly covers both lexical complexity prediction (LCP) annotations and lexical simplification (LS) for Romanian, along with a comparison of lexical simplification approaches. We propose a methodology for ordering simplification suggestions using a pairwise ranking approximation method, arranging candidates from simple to complex based on a separate set of human judgments. In addition, we provide human lexical complexity annotations for 3,921 word samples in context. Finally, we explore several novel pipelines for complexity prediction and simplification and present the first text simplification system for Romanian.