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Reference-less Quality Estimation of Text Simplification Systems

Reference-less Quality Estimation of Text Simplification Systems

Authors :
Pierre-Emmanuel Mazaré
Samuel Humeau
Éric Villemonte de la Clergerie
Antoine Bordes
Benoît Sagot
Louis Martin
Facebook AI Research [Paris] (FAIR)
Facebook
Automatic Language Modelling and ANAlysis & Computational Humanities (ALMAnaCH)
Inria de Paris
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Source :
1st Workshop on Automatic Text Adaptation (ATA), 1st Workshop on Automatic Text Adaptation (ATA), Nov 2018, Tilburg, Netherlands
Publication Year :
2019

Abstract

International audience; The evaluation of text simplification (TS) systems remains an open challenge. As the task has common points with machine translation (MT), TS is often evaluated using MT metrics such as BLEU. However, such metrics require high quality reference data, which is rarely available for TS. TS has the advantage over MT of being a monolingual task, which allows for direct comparisons to be made between the simplified text and its original version. In this paper, we compare multiple approaches to reference-less quality estimation of sentence-level text simplification systems, based on the dataset used for the QATS 2016 shared task. We distinguish three different dimensions: gram-maticality, meaning preservation and simplicity. We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.

Details

Language :
English
Database :
OpenAIRE
Journal :
1st Workshop on Automatic Text Adaptation (ATA), 1st Workshop on Automatic Text Adaptation (ATA), Nov 2018, Tilburg, Netherlands
Accession number :
edsair.doi.dedup.....dc47270f44e94550e8dcef574e57f794