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    <abstract>Discourse relations contribute to the structure of a text and can optionally be realized through explicit connectives such as “but” and “while”. But when are these connectives necessary to avoid possible misunderstandings? We investigate this question by first building a corpus of 4,274 text revisions in each of which a connective was explicitly inserted. For a subset of 250 cases, we collect plausibility annotations on other connectives to check whether they would represent suitable alternative relations. The results of this annotation show that several relations are often perceived as plausible in our data. Furthermore, we analyze the extent to which large language models can identify instances with multiple plausible relations as a possible source of misunderstandings. We find that the models predict plausibility of individual connectives with up to 66% accuracy, but they are not reliable in estimating when multiple relations are plausible.</abstract>
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    <identifier type="doi">10.18653/v1/2025.findings-acl.633</identifier>
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%0 Conference Proceedings
%T Clarifying Underspecified Discourse Relations in Instructional Texts
%A Aktas, Berfin
%A Roth, Michael
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F aktas-roth-2025-clarifying
%X Discourse relations contribute to the structure of a text and can optionally be realized through explicit connectives such as “but” and “while”. But when are these connectives necessary to avoid possible misunderstandings? We investigate this question by first building a corpus of 4,274 text revisions in each of which a connective was explicitly inserted. For a subset of 250 cases, we collect plausibility annotations on other connectives to check whether they would represent suitable alternative relations. The results of this annotation show that several relations are often perceived as plausible in our data. Furthermore, we analyze the extent to which large language models can identify instances with multiple plausible relations as a possible source of misunderstandings. We find that the models predict plausibility of individual connectives with up to 66% accuracy, but they are not reliable in estimating when multiple relations are plausible.
%R 10.18653/v1/2025.findings-acl.633
%U https://aclanthology.org/2025.findings-acl.633/
%U https://doi.org/10.18653/v1/2025.findings-acl.633
%P 12237-12256

Markdown (Informal)

[Clarifying Underspecified Discourse Relations in Instructional Texts](https://aclanthology.org/2025.findings-acl.633/) (Aktas & Roth, Findings 2025)

• Clarifying Underspecified Discourse Relations in Instructional Texts (Aktas & Roth, Findings 2025)

ACL

• Berfin Aktas and Michael Roth. 2025. Clarifying Underspecified Discourse Relations in Instructional Texts. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12237–12256, Vienna, Austria. Association for Computational Linguistics.