---
res:
  bibo_abstract:
  - To produce explanations that are more likely to be accepted by humans, Explainable
    Artificial Intelligence (XAI) systems need to incorporate explanation models grounded
    in human communication patterns. So far, little is known about how an explainee,
    who lacks understanding of an issue, and an explainer, who has knowledge to fill
    the explainee's knowledge gap, actively shape an explanation process, and how
    their involvement relates to explanatory success in terms of maximizing the explainee's
    level of understanding. In this paper, we characterize explanations as dialogues
    in which explainee and explainer take turns to advance the explanation process.
    We build on an existing annotation scheme of ‘explanatory moves’ to characterize
    such turns, and manually annotate 362 dialogical explanations from the “Explain
    Like I'm Five” subreddit. Building on the annotated data, we compute correlations
    between explanatory moves and explanatory success, measured on a five-point Likert
    scale, in order to identify factors that are significantly correlated with explanatory
    success. Based on a qualitative analysis of these factors, we develop a conceptual
    model of the main factors that contribute to the success of explanatory dialogues.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Meisam
      foaf_name: Booshehri, Meisam
      foaf_surname: Booshehri
  - foaf_Person:
      foaf_givenName: Hendrik
      foaf_name: Buschmeier, Hendrik
      foaf_surname: Buschmeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=76456
    orcid: 0000-0002-9613-5713
  - foaf_Person:
      foaf_givenName: Philipp
      foaf_name: Cimiano, Philipp
      foaf_surname: Cimiano
  bibo_doi: 10.1145/3678957.3685744
  dct_date: 2024^xs_gYear
  dct_language: eng
  dct_publisher: ACM@
  dct_title: A model of factors contributing to the success of dialogical explanations@
...