@inproceedings{maes-etal-2024-get,
    title = "Did You Get It? A Zero-Shot Approach to Locate Information Transfers in Conversations",
    author = {Ma{\"e}s, Eliot  and
      Boudraa, Hossam  and
      Blache, Philippe  and
      Becerra-Bonache, Leonor},
    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.437",
    pages = "4877--4890",
    abstract = "Interaction theories suggest that the emergence of mutual understanding between speakers in natural conversations depends on the construction of a shared knowledge base (\textit{common ground}), but the details of which information and the circumstances under which it is memorized are not explained by any model. Previous works have looked at metrics derived from Information Theory to quantify the dynamics of information exchanged between participants, but do not provide an efficient way to locate information that will enter the common ground. We propose a new method based on the segmentation of a conversation into themes followed by their summarization. We then obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. We evaluate two Large Language Models (LLMs) on this pipeline, on the French conversational corpus Paco-Cheese. More generally, we explore how the recent developments in the field of LLMs provide us with the means to implement these new methods and more generally support research into questions that usually heavily relies on human annotators.",
}

Interaction theories suggest that the emergence of mutual understanding between speakers in natural conversations depends on the construction of a shared knowledge base (\textit{common ground}), but the details of which information and the circumstances under which it is memorized are not explained by any model. Previous works have looked at metrics derived from Information Theory to quantify the dynamics of information exchanged between participants, but do not provide an efficient way to locate information that will enter the common ground. We propose a new method based on the segmentation of a conversation into themes followed by their summarization. We then obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. We evaluate two Large Language Models (LLMs) on this pipeline, on the French conversational corpus Paco-Cheese. More generally, we explore how the recent developments in the field of LLMs provide us with the means to implement these new methods and more generally support research into questions that usually heavily relies on human annotators.
Ma{\"e}s, Eliot Boudraa, Hossam Blache, Philippe Becerra-Bonache, Leonor
Did You Get It? A Zero-Shot Approach to Locate Information Transfers in Conversations