Since paraphrasing is an ill-defined task, the term ``paraphrasing'' covers text transformation tasks with different characteristics. Consequently, existing paraphrasing studies have applied quite different (explicit and implicit) criteria as to when a pair of texts is to be considered a paraphrase, all of which amount to postulating a certain level of semantic or lexical similarity. In this paper, we conduct a literature review and propose a taxonomy to organize the 25~identified paraphrasing (sub-)tasks. Using classifiers trained to identify the tasks that a given paraphrasing instance fits, we find that the distributions of task-specific instances in the known paraphrase corpora vary substantially. This means that the use of these corpora, without the respective paraphrase conditions being clearly defined (which is the normal case), must lead to incomparable and misleading results.
%0 Journal Article
%1 Gohsen2024-ju
%A Gohsen, Marcel
%A Hagen, Matthias
%A Potthast, Martin
%A Stein, Benno
%D 2024
%I arXiv
%K
%T Task-Oriented Paraphrase Analytics
%X Since paraphrasing is an ill-defined task, the term ``paraphrasing'' covers text transformation tasks with different characteristics. Consequently, existing paraphrasing studies have applied quite different (explicit and implicit) criteria as to when a pair of texts is to be considered a paraphrase, all of which amount to postulating a certain level of semantic or lexical similarity. In this paper, we conduct a literature review and propose a taxonomy to organize the 25~identified paraphrasing (sub-)tasks. Using classifiers trained to identify the tasks that a given paraphrasing instance fits, we find that the distributions of task-specific instances in the known paraphrase corpora vary substantially. This means that the use of these corpora, without the respective paraphrase conditions being clearly defined (which is the normal case), must lead to incomparable and misleading results.
@article{Gohsen2024-ju,
abstract = {Since paraphrasing is an ill-defined task, the term ``paraphrasing'' covers text transformation tasks with different characteristics. Consequently, existing paraphrasing studies have applied quite different (explicit and implicit) criteria as to when a pair of texts is to be considered a paraphrase, all of which amount to postulating a certain level of semantic or lexical similarity. In this paper, we conduct a literature review and propose a taxonomy to organize the 25~identified paraphrasing (sub-)tasks. Using classifiers trained to identify the tasks that a given paraphrasing instance fits, we find that the distributions of task-specific instances in the known paraphrase corpora vary substantially. This means that the use of these corpora, without the respective paraphrase conditions being clearly defined (which is the normal case), must lead to incomparable and misleading results.},
added-at = {2024-09-10T10:41:24.000+0200},
author = {Gohsen, Marcel and Hagen, Matthias and Potthast, Martin and Stein, Benno},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2f59d31fe301e9a6088685098c507c3df/scadsfct},
interhash = {cce49e6ed661c73f8fc0a21b9f94e291},
intrahash = {f59d31fe301e9a6088685098c507c3df},
keywords = {},
publisher = {arXiv},
timestamp = {2024-09-10T10:47:32.000+0200},
title = {{Task-Oriented} Paraphrase Analytics},
year = 2024
}
