EASSE: Easier Automatic Sentence Simplification Evaluation

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), 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 (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computational Linguistics
Last Modified:	04 Apr 2022 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/147263

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