aiqbench analysis
This paper establishes the foundational dataset and baselines necessary for advancing natural language processing in Romanian, specifically targeting lexical complexity prediction and simplification tasks. By introducing novel pipelines for ordering suggestions based on human judgments, it directly contributes to machine learning research aimed at improving accessibility through automated text adaptation. The work represents a significant step forward in low-resource NLP applications within the broader field of computational linguistics.
RALS: Resources and Baselines for Romanian Automatic Lexical Simplification
arXiv:2607.20078v1 Announce Type: new Abstract: 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.
Abstract: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.
Computation and Language (cs.CL)
arXiv:2607.20078 [cs.CL]
(or arXiv:2607.20078v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20078 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 185 words
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