---
res:
  bibo_abstract:
  - As AI is more and more pervasive in everyday life, humans have an increasing demand
    to understand its behavior and decisions. Most research on explainable AI builds
    on the premise that there is one ideal explanation to be found. In fact, however,
    everyday explanations are co-constructed in a dialogue between the person explaining
    (the explainer) and the specific person being explained to (the explainee). In
    this paper, we introduce a first corpus of dialogical explanations to enable NLP
    research on how humans explain as well as on how AI can learn to imitate this
    process. The corpus consists of 65 transcribed English dialogues from the Wired
    video series 5 Levels, explaining 13 topics to five explainees of different proficiency.
    All 1550 dialogue turns have been manually labeled by five independent professionals
    for the topic discussed as well as for the dialogue act and the explanation move
    performed. We analyze linguistic patterns of explainers and explainees, and we
    explore differences across proficiency levels. BERT-based baseline results indicate
    that sequence information helps predicting topics, acts, and moves effectively.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Henning
      foaf_name: Wachsmuth, Henning
      foaf_surname: Wachsmuth
      foaf_workInfoHomepage: http://www.librecat.org/personId=3900
  - foaf_Person:
      foaf_givenName: Milad
      foaf_name: Alshomary, Milad
      foaf_surname: Alshomary
      foaf_workInfoHomepage: http://www.librecat.org/personId=73059
  dct_date: 2022^xs_gYear
  dct_language: eng
  dct_publisher: International Committee on Computational Linguistics@
  dct_title: '“Mama Always Had a Way of Explaining Things So I Could Understand”:
    A Dialogue Corpus for Learning to Construct Explanations@'
...