@inproceedings{scarton-etal-2017-musst,
title = "{MUSST}: A Multilingual Syntactic Simplification Tool",
author = "Scarton, Carolina and
Palmero Aprosio, Alessio and
Tonelli, Sara and
Mart{\'\i}n Wanton, Tamara and
Specia, Lucia",
editor = "Park, Seong-Bae and
Supnithi, Thepchai",
booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
month = nov,
year = "2017",
address = "Tapei, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/I17-3007",
pages = "25--28",
abstract = "We describe MUSST, a multilingual syntactic simplification tool. The tool supports sentence simplifications for English, Italian and Spanish, and can be easily extended to other languages. Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications). The tool was implemented in the context of the European project SIMPATICO on text simplification for Public Administration (PA) texts. Our evaluation on sentences in the PA domain shows that we obtain correct simplifications for 76{\%} of the simplified cases in English, 71{\%} of the cases in Spanish. For Italian, the results are lower (38{\%}) but the tool is still under development.",
}
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<abstract>We describe MUSST, a multilingual syntactic simplification tool. The tool supports sentence simplifications for English, Italian and Spanish, and can be easily extended to other languages. Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications). The tool was implemented in the context of the European project SIMPATICO on text simplification for Public Administration (PA) texts. Our evaluation on sentences in the PA domain shows that we obtain correct simplifications for 76% of the simplified cases in English, 71% of the cases in Spanish. For Italian, the results are lower (38%) but the tool is still under development.</abstract>
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%0 Conference Proceedings
%T MUSST: A Multilingual Syntactic Simplification Tool
%A Scarton, Carolina
%A Palmero Aprosio, Alessio
%A Tonelli, Sara
%A Martín Wanton, Tamara
%A Specia, Lucia
%Y Park, Seong-Bae
%Y Supnithi, Thepchai
%S Proceedings of the IJCNLP 2017, System Demonstrations
%D 2017
%8 November
%I Association for Computational Linguistics
%C Tapei, Taiwan
%F scarton-etal-2017-musst
%X We describe MUSST, a multilingual syntactic simplification tool. The tool supports sentence simplifications for English, Italian and Spanish, and can be easily extended to other languages. Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications). The tool was implemented in the context of the European project SIMPATICO on text simplification for Public Administration (PA) texts. Our evaluation on sentences in the PA domain shows that we obtain correct simplifications for 76% of the simplified cases in English, 71% of the cases in Spanish. For Italian, the results are lower (38%) but the tool is still under development.
%U https://aclanthology.org/I17-3007
%P 25-28
Markdown (Informal)
[MUSST: A Multilingual Syntactic Simplification Tool](https://aclanthology.org/I17-3007) (Scarton et al., IJCNLP 2017)
ACL
- Carolina Scarton, Alessio Palmero Aprosio, Sara Tonelli, Tamara Martín Wanton, and Lucia Specia. 2017. MUSST: A Multilingual Syntactic Simplification Tool. In Proceedings of the IJCNLP 2017, System Demonstrations, pages 25–28, Tapei, Taiwan. Association for Computational Linguistics.