@inproceedings{alshomary-etal-2024-modeling,
    title = "Modeling the Quality of Dialogical Explanations",
    author = "Alshomary, Milad  and
      Lange, Felix  and
      Booshehri, Meisam  and
      Sengupta, Meghdut  and
      Cimiano, Philipp  and
      Wachsmuth, Henning",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1007",
    pages = "11523--11536",
    abstract = "Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an \textit{explainer} discusses a concept or phenomenon of interest with an \textit{explainee}. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee{'}s side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum \textit{Explain Like I am Five} and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.",
}

Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an \textit{explainer} discusses a concept or phenomenon of interest with an \textit{explainee}. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee{'}s side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum \textit{Explain Like I am Five} and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.
Alshomary, Milad Lange, Felix Booshehri, Meisam Sengupta, Meghdut Cimiano, Philipp Wachsmuth, Henning
Modeling the Quality of Dialogical Explanations