Alva Manchego, Fernando, Martin, Louis, Scarton, Carolina and Specia, Lucia
2019.
EASSE: Easier Automatic Sentence Simplification Evaluation.
Presented at: 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP),
Hong Kong, China,
3-7 November 2019.
Published in: Pado, Sebastian and Huang, Ruihong eds.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations.
Association for Computational Linguistics,
pp. 49-54.
10.18653/v1/D19-3009
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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.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Association for Computational Linguistics |
| ISBN: | 978-195073792-5 |
| Date of First Compliant Deposit: | 28 April 2026 |
| Last Modified: | 28 Apr 2026 13:23 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/147263 |
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