@inproceedings{yamaguchi-etal-2024-automatic-decomposition,
title = "Automatic Decomposition of Text Editing Examples into Primitive Edit Operations: Toward Analytic Evaluation of Editing Systems",
author = "Yamaguchi, Daichi and
Miyata, Rei and
Fujita, Atsushi and
Kajiwara, Tomoyuki and
Sato, Satoshi",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.170",
pages = "1899--1914",
abstract = "This paper presents our work on a task of automatic decomposition of text editing examples into primitive edit operations. Toward a detailed analysis of the behavior of text editing systems, identification of fine-grained edit operations performed by the systems is essential. Given a pair of source and edited sentences, the goal of our task is to generate a non-redundant sequence of primitive edit operations, i.e., the semantically minimal edit operations preserving grammaticality, that iteratively converts the source sentence to the edited sentence. First, we formalize this task, explaining its significant features and specifying the constraints that primitive edit operations should satisfy. Then, we propose a method to automate this task, which consists of two steps: generation of an edit operation lattice and selection of an optimal path. To obtain a wide range of edit operation candidates in the first step, we combine a phrase aligner and a large language model. Experimental results show that our method perfectly decomposes 44{\%} and 64{\%} of editing examples in the text simplification and machine translation post-editing datasets, respectively. Detailed analyses also provide insights into the difficulties of this task, suggesting directions for improvement.",
}
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%0 Conference Proceedings
%T Automatic Decomposition of Text Editing Examples into Primitive Edit Operations: Toward Analytic Evaluation of Editing Systems
%A Yamaguchi, Daichi
%A Miyata, Rei
%A Fujita, Atsushi
%A Kajiwara, Tomoyuki
%A Sato, Satoshi
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F yamaguchi-etal-2024-automatic-decomposition
%X This paper presents our work on a task of automatic decomposition of text editing examples into primitive edit operations. Toward a detailed analysis of the behavior of text editing systems, identification of fine-grained edit operations performed by the systems is essential. Given a pair of source and edited sentences, the goal of our task is to generate a non-redundant sequence of primitive edit operations, i.e., the semantically minimal edit operations preserving grammaticality, that iteratively converts the source sentence to the edited sentence. First, we formalize this task, explaining its significant features and specifying the constraints that primitive edit operations should satisfy. Then, we propose a method to automate this task, which consists of two steps: generation of an edit operation lattice and selection of an optimal path. To obtain a wide range of edit operation candidates in the first step, we combine a phrase aligner and a large language model. Experimental results show that our method perfectly decomposes 44% and 64% of editing examples in the text simplification and machine translation post-editing datasets, respectively. Detailed analyses also provide insights into the difficulties of this task, suggesting directions for improvement.
%U https://aclanthology.org/2024.lrec-main.170
%P 1899-1914
Markdown (Informal)
[Automatic Decomposition of Text Editing Examples into Primitive Edit Operations: Toward Analytic Evaluation of Editing Systems](https://aclanthology.org/2024.lrec-main.170) (Yamaguchi et al., LREC-COLING 2024)
ACL