EASSE: Easier Automatic Sentence Simplification Evaluation
This work addresses the need for standardized evaluation tools in the sentence simplification domain, but it is incremental as it builds on existing metrics and data.
The authors tackled the problem of evaluating sentence simplification systems by introducing EASSE, a Python package that standardizes automatic evaluation, and they demonstrated that it enables better comparison and understanding of system performance.
We 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.