EASSE: Easier Automatic Sentence Simplification Evaluation

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

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.
Anthology ID:
D19-3009
Volume:
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
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Sebastian Padó, Ruihong Huang
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
49–54
Language:
URL:
https://aclanthology.org/D19-3009
DOI:
10.18653/v1/D19-3009
Bibkey:
Cite (ACL):
Fernando Alva-Manchego, Louis Martin, Carolina Scarton, and Lucia Specia. 2019. EASSE: Easier Automatic Sentence Simplification Evaluation. In 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, pages 49–54, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
EASSE: Easier Automatic Sentence Simplification Evaluation (Alva-Manchego et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/D19-3009.pdf
Code
 feralvam/easse
Data
TurkCorpus
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