@inproceedings{alva-manchego-etal-2019-easse,
title = "{EASSE}: Easier Automatic Sentence Simplification Evaluation",
author = "Alva-Manchego, Fernando and
Martin, Louis and
Scarton, Carolina and
Specia, Lucia",
editor = "Pad{\'o}, Sebastian and
Huang, Ruihong",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-3009",
doi = "10.18653/v1/D19-3009",
pages = "49--54",
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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%0 Conference Proceedings
%T EASSE: Easier Automatic Sentence Simplification Evaluation
%A Alva-Manchego, Fernando
%A Martin, Louis
%A Scarton, Carolina
%A Specia, Lucia
%Y Padó, Sebastian
%Y Huang, Ruihong
%S 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
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F alva-manchego-etal-2019-easse
%X 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.
%R 10.18653/v1/D19-3009
%U https://aclanthology.org/D19-3009
%U https://doi.org/10.18653/v1/D19-3009
%P 49-54
Markdown (Informal)
[EASSE: Easier Automatic Sentence Simplification Evaluation](https://aclanthology.org/D19-3009) (Alva-Manchego et al., EMNLP-IJCNLP 2019)
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.