![]() Based on plausibility judgements for each clarification, we define the task of distinguishing between plausible and implausible clarifications. We show how language modeling can be used to generate alternate clarifications, which may or may not be compatible with the human clarification. Our data set, henceforth called CLAIRE, is based on a corpus of revision histories from wikiHow, from which we extract human clarifications that resolve an implicit or underspecified phrase. In this paper, we present a data set of such phrases in English from instructional texts together with multiple possible clarifications. Publisher = "European Language Resources Association",Ībstract = "Natural language inherently consists of implicit and underspecified phrases, which represent potential sources of misunderstanding. Cite (Informal): Clarifying Implicit and Underspecified Phrases in Instructional Text (Anthonio et al., LREC 2022) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: = "Clarifying Implicit and Underspecified Phrases in Instructional Text",īooktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3319–3330, Marseille, France. Clarifying Implicit and Underspecified Phrases in Instructional Text. Anthology ID: 2022.lrec-1.354 Volume: Proceedings of the Thirteenth Language Resources and Evaluation Conference Month: June Year: 2022 Address: Marseille, France Venue: LREC SIG: Publisher: European Language Resources Association Note: Pages: 3319–3330 Language: URL: DOI: Bibkey: anthonio-etal-2022-clarifying Cite (ACL): Talita Anthonio, Anna Sauer, and Michael Roth. We provide several baseline models for this task and analyze to what extent different clarifications represent multiple readings as a first step to investigate misunderstandings caused by implicit/underspecified language in instructional texts. Abstract Natural language inherently consists of implicit and underspecified phrases, which represent potential sources of misunderstanding.
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