@inproceedings{kwak-etal-2023-context,
title = "Context and Literacy Aware Learnable Metric for Text Simplification",
author = "Kwak, Jeongwon and
Park, Hyeryun and
Kim, Kyungmo and
Choi, Jinwook",
editor = "Gehrmann, Sebastian and
Wang, Alex and
Sedoc, Jo{\~a}o and
Clark, Elizabeth and
Dhole, Kaustubh and
Chandu, Khyathi Raghavi and
Santus, Enrico and
Sedghamiz, Hooman",
booktitle = "Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.gem-1.15",
pages = "175--180",
abstract = "Automatic evaluation of text simplification is important; but assessing its transformation into simpler sentences can be challenging for various reasons. However, the most commonly used metric in text simplification, SARI, fails to capture the difficulty of generating words that are not present in the references, regardless of their meaning. We propose a new learnable evaluation metric that decomposes and reconstructs sentences to simultaneously measure the similarity and difficulty of sentences within a single system. Through experiments, we confirm that it exhibited the highest similarity in correlation with the human evaluation.",
}
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<abstract>Automatic evaluation of text simplification is important; but assessing its transformation into simpler sentences can be challenging for various reasons. However, the most commonly used metric in text simplification, SARI, fails to capture the difficulty of generating words that are not present in the references, regardless of their meaning. We propose a new learnable evaluation metric that decomposes and reconstructs sentences to simultaneously measure the similarity and difficulty of sentences within a single system. Through experiments, we confirm that it exhibited the highest similarity in correlation with the human evaluation.</abstract>
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%0 Conference Proceedings
%T Context and Literacy Aware Learnable Metric for Text Simplification
%A Kwak, Jeongwon
%A Park, Hyeryun
%A Kim, Kyungmo
%A Choi, Jinwook
%Y Gehrmann, Sebastian
%Y Wang, Alex
%Y Sedoc, João
%Y Clark, Elizabeth
%Y Dhole, Kaustubh
%Y Chandu, Khyathi Raghavi
%Y Santus, Enrico
%Y Sedghamiz, Hooman
%S Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kwak-etal-2023-context
%X Automatic evaluation of text simplification is important; but assessing its transformation into simpler sentences can be challenging for various reasons. However, the most commonly used metric in text simplification, SARI, fails to capture the difficulty of generating words that are not present in the references, regardless of their meaning. We propose a new learnable evaluation metric that decomposes and reconstructs sentences to simultaneously measure the similarity and difficulty of sentences within a single system. Through experiments, we confirm that it exhibited the highest similarity in correlation with the human evaluation.
%U https://aclanthology.org/2023.gem-1.15
%P 175-180
Markdown (Informal)
[Context and Literacy Aware Learnable Metric for Text Simplification](https://aclanthology.org/2023.gem-1.15) (Kwak et al., GEM-WS 2023)
ACL