@inproceedings{yang-2024-trusted,
    title = "A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning",
    author = "Yang, Shuo",
    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.152",
    pages = "1722--1733",
    abstract = "While deep learning models are powerful, they have limitations in tasks that require commonsense reasoning, as these tasks often involve interpreting information that may not be directly available in the input. Providing evidence has been proven to significantly enhance performance in commonsense reasoning tasks. However, there are various perspectives on evidence, including natural language explanations generated by pre-trained language models, facts derived from world knowledge like text corpora and knowledge bases, and rationales extracted from the input context. Hence, it is crucial to determine how to estimate the confidence degree of different evidence and how to combine them reliably. To address these challenges, this study proposes a trusted multi-view evidential fusion framework for reliable commonsense reasoning tasks that dynamically assesses the confidence of evidence and combines different views of evidence in a trustworthy manner. The proposed method is applied to three commonsense question-answering benchmarks, demonstrating that this approach can effectively reason with multi-view evidence and can compete with state-of-the-art performance.",
}

While deep learning models are powerful, they have limitations in tasks that require commonsense reasoning, as these tasks often involve interpreting information that may not be directly available in the input. Providing evidence has been proven to significantly enhance performance in commonsense reasoning tasks. However, there are various perspectives on evidence, including natural language explanations generated by pre-trained language models, facts derived from world knowledge like text corpora and knowledge bases, and rationales extracted from the input context. Hence, it is crucial to determine how to estimate the confidence degree of different evidence and how to combine them reliably. To address these challenges, this study proposes a trusted multi-view evidential fusion framework for reliable commonsense reasoning tasks that dynamically assesses the confidence of evidence and combines different views of evidence in a trustworthy manner. The proposed method is applied to three commonsense question-answering benchmarks, demonstrating that this approach can effectively reason with multi-view evidence and can compete with state-of-the-art performance.
Yang, Shuo
A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning