Characterizing the response space of questions: data and theory - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue Dialogue & Discourse Année : 2022

Characterizing the response space of questions: data and theory

Résumé

The main aim of this paper is to provide a characterization of the response space for questions using a taxonomy grounded in a dialogical formal semantics. As a starting point we take the typology for responses in the form of questions provided in \cite{lupginz-jlm}. This work develops a wide coverage taxonomy for question/question sequences observable in corpora including the BNC, CHILDES, and BEE, as well as formal modeling of all the postulated classes. Our aim is to extend this work to cover \emph{all} responses to questions. We present the extended typology of responses to questions based on a corpus studies of BNC, BEE, Maptask and CornellMovie with include 506, 262, 467, and 678 question/response pairs respectively. We compare the data for English with data from Polish using the Spokes corpus (694 question/response pairs). We discuss annotation reliability and disagreement analysis. We sketch how each class can be formalized using a dialogical semantics appropriate for dialogue management.

Domaines

Linguistique

Dates et versions

hal-03992293 , version 1 (16-02-2023)

Identifiants

Citer

Jonathan Ginzburg, Zulipiye Yusupujiang, Chuyuan Li, Kexin Ren, Aleksandra Kucharska, et al.. Characterizing the response space of questions: data and theory. Dialogue & Discourse, 2022, 13, pp.79-132. ⟨10.5210/dad.2022.203⟩. ⟨hal-03992293⟩
17 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More