2021 IJCNLP IJCNLP 2021

Flesch-Kincaid is Not a Text Simplification Evaluation Metric

Abstract

AbstractSentence-level text simplification is currently evaluated using both automated metrics and human evaluation. For automatic evaluation, a combination of metrics is usually employed to evaluate different aspects of the simplification. Flesch-Kincaid Grade Level (FKGL) is one metric that has been regularly used to measure the readability of system output. In this paper, we argue that FKGL should not be used to evaluate text simplification systems. We provide experimental analyses on recent system output showing that the FKGL score can easily be manipulated to improve the score dramatically with only minor impact on other automated metrics (BLEU and SARI). Instead of using FKGL, we suggest that the component statistics, along with others, be used for posthoc analysis to understand system behavior.

🌉 Interdisciplinary Bridge — Interdisciplinary and Natural Language Processing
🧭 Keyword Pioneer — sari score
🐣 Hot Topic Early Bird — text simplification
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio