EASSE: Easier Automatic Sentence Simplification Evaluation
August 13, 2019 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
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Authors
Fernando Alva-Manchego, Louis Martin, Carolina Scarton, Lucia Specia
arXiv ID
1908.04567
Category
cs.CL: Computation & Language
Citations
144
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
4 months ago
Abstract
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.
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