@inproceedings{choshen-abend-2018-reference,
title = "Reference-less Measure of Faithfulness for Grammatical Error Correction",
author = "Choshen, Leshem and
Abend, Omri",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2020",
doi = "10.18653/v1/N18-2020",
pages = "124--129",
abstract = "We propose USim, a semantic measure for Grammatical Error Correction (that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output{'}s grammaticality. USim operates by comparing the semantic symbolic structure of the source and the correction, without relying on manually-curated references. Our experiments establish the validity of USim, by showing that the semantic structures can be consistently applied to ungrammatical text, that valid corrections obtain a high USim similarity score to the source, and that invalid corrections obtain a lower score.",
}
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%0 Conference Proceedings
%T Reference-less Measure of Faithfulness for Grammatical Error Correction
%A Choshen, Leshem
%A Abend, Omri
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F choshen-abend-2018-reference
%X We propose USim, a semantic measure for Grammatical Error Correction (that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output’s grammaticality. USim operates by comparing the semantic symbolic structure of the source and the correction, without relying on manually-curated references. Our experiments establish the validity of USim, by showing that the semantic structures can be consistently applied to ungrammatical text, that valid corrections obtain a high USim similarity score to the source, and that invalid corrections obtain a lower score.
%R 10.18653/v1/N18-2020
%U https://aclanthology.org/N18-2020
%U https://doi.org/10.18653/v1/N18-2020
%P 124-129
Markdown (Informal)
[Reference-less Measure of Faithfulness for Grammatical Error Correction](https://aclanthology.org/N18-2020) (Choshen & Abend, NAACL 2018)
ACL
- Leshem Choshen and Omri Abend. 2018. Reference-less Measure of Faithfulness for Grammatical Error Correction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 124–129, New Orleans, Louisiana. Association for Computational Linguistics.