@inproceedings{zhang-etal-2020-sema,
title = "{SEMA}: Text Simplification Evaluation through Semantic Alignment",
author = "Zhang, Xuan and
Zhao, Huizhou and
Zhang, KeXin and
Zhang, Yiyang",
editor = "YANG, Erhong and
XUN, Endong and
ZHANG, Baolin and
RAO, Gaoqi",
booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlptea-1.17",
pages = "121--128",
abstract = "Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.",
}
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<abstract>Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.</abstract>
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%0 Conference Proceedings
%T SEMA: Text Simplification Evaluation through Semantic Alignment
%A Zhang, Xuan
%A Zhao, Huizhou
%A Zhang, KeXin
%A Zhang, Yiyang
%Y YANG, Erhong
%Y XUN, Endong
%Y ZHANG, Baolin
%Y RAO, Gaoqi
%S Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F zhang-etal-2020-sema
%X Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.
%U https://aclanthology.org/2020.nlptea-1.17
%P 121-128
Markdown (Informal)
[SEMA: Text Simplification Evaluation through Semantic Alignment](https://aclanthology.org/2020.nlptea-1.17) (Zhang et al., NLP-TEA 2020)
ACL