@inproceedings{gorska-etal-2024-forecast2023,
    title = "{FORECAST}2023: A Forecast and Reasoning Corpus of Argumentation Structures",
    author = "G{\'o}rska, Kamila  and
      Lawrence, John  and
      Reed, Chris",
    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.652",
    pages = "7395--7405",
    abstract = "It is known from large-scale crowd experimentation that some people are innately better at analysing complex situations and making justified predictions {--} the so-called {`}superforecasters{'}. Surprisingly, however, there has to date been no work exploring the role played by the reasoning in those justifications. Bag-of-words analyses might tell us something, but the real value lies in understanding what features of reasoning and argumentation lead to better forecasts {--} both in providing an objective measure for argument quality, and even more importantly, in providing guidance on how to improve forecasting performance. The work presented here covers the creation of a unique dataset of such prediction rationales, the structure of which naturally lends itself to partially automated annotation which in turn is used as the basis for subsequent manual enhancement that provides a uniquely fine-grained and close characterisation of the structure of argumentation, with potential impact on forecasting domains from intelligence analysis to investment decision-making.",
}

It is known from large-scale crowd experimentation that some people are innately better at analysing complex situations and making justified predictions {--} the so-called {`}superforecasters{'}. Surprisingly, however, there has to date been no work exploring the role played by the reasoning in those justifications. Bag-of-words analyses might tell us something, but the real value lies in understanding what features of reasoning and argumentation lead to better forecasts {--} both in providing an objective measure for argument quality, and even more importantly, in providing guidance on how to improve forecasting performance. The work presented here covers the creation of a unique dataset of such prediction rationales, the structure of which naturally lends itself to partially automated annotation which in turn is used as the basis for subsequent manual enhancement that provides a uniquely fine-grained and close characterisation of the structure of argumentation, with potential impact on forecasting domains from intelligence analysis to investment decision-making.
G{\'o}rska, Kamila Lawrence, John Reed, Chris
FORECAST2023: A Forecast and Reasoning Corpus of Argumentation Structures