2019 EMNLP EMNLP 2019

EASSE: Easier Automatic Sentence Simplification Evaluation

Abstract

AbstractWe introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single access point to a broad range of evaluation resources: standard automatic metrics for assessing SS outputs (e.g. SARI), word-level accuracy scores for certain simplification transformations, reference-independent quality estimation features (e.g. compression ratio), and standard test data for SS evaluation (e.g. TurkCorpus). Finally, EASSE generates easy-to-visualise reports on the various metrics and features above and on how a particular SS output fares against reference simplifications. Through experiments, we show that these functionalities allow for better comparison and understanding of the performance of SS systems.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Computer Vision and Machine Learning and Natural Language Processing
📈 Trend Setter — Text Simplification
🧭 Keyword Pioneer — sari metric
🐣 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