EASSE: Easier Automatic Sentence Simplification Evaluation

Conference on Empirical Methods in Natural Language Processing (EMNLP)

By: Fernando Alva-Manchego, Louis Martin, Carolina Scarton, Lucia Specia

Abstract

We introduce EASSE, a Python package aiming to facilitate and standardize 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-visualize 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 under