Characterizing the Response Space of Questions: data and theory

Abstract

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.

Bullshit, Pragmatic Deception, and Natural Language Processing

“…Previously, the QUD framework was used to annotate discourse structure (De Kuthy et al, 2018), automatically generate potential QUDs from assertions (De Kuthy et al, 2020), as well as generating QUDs evoked by the preceding context, in essence predicting current questions and the following discourse (Westera et al, 2020). The Ginzburg et al (2022) paper, mentioned above, also uses QUDs as a basis for analyzing the relevance of responses; building on Ginzburg (2012), the authors provide a formal analysis of both cooperative and uncooperative discourse or questionanswering. The QUD framework -and not other pragmatic models that may entail deception such as Asher et al (2017); Asher and Paul (2018) -was chosen as a basis for computational bullshit detection for two main reasons: Firstly, the definition by Stokke and Fallis is a good starting point and is already based on QUDs.…”