@inproceedings{ghazarian-etal-2023-accent,
    title = "{ACCENT}: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems",
    author = "Ghazarian, Sarik  and
      Shao, Yijia  and
      Han, Rujun  and
      Galstyan, Aram  and
      Peng, Nanyun",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.241",
    doi = "10.18653/v1/2023.acl-long.241",
    pages = "4398--4419",
    abstract = "Commonsense reasoning is omnipresent in human communications and thus is an important feature for open-domain dialogue systems. However, evaluating commonsense in dialogue systems is still an open challenge. We take the first step by focusing on \textit{event commonsense} that considers events and their relations, and is crucial in both dialogues and general commonsense reasoning. We propose \textbf{ACCENT}, an event commonsense evaluation metric empowered by commonsense knowledge bases (CSKBs). ACCENT first extracts event-relation tuples from a dialogue, and then evaluates the response by scoring the tuples in terms of their compatibility with the CSKB. To evaluate ACCENT, we construct the first public event commonsense evaluation dataset for open-domain dialogues.Our experiments show that ACCENT is an efficient metric for event commonsense evaluation, which achieves higher correlations with human judgments than existing baselines.",
}

Commonsense reasoning is omnipresent in human communications and thus is an important feature for open-domain dialogue systems. However, evaluating commonsense in dialogue systems is still an open challenge. We take the first step by focusing on \textit{event commonsense} that considers events and their relations, and is crucial in both dialogues and general commonsense reasoning. We propose \textbf{ACCENT}, an event commonsense evaluation metric empowered by commonsense knowledge bases (CSKBs). ACCENT first extracts event-relation tuples from a dialogue, and then evaluates the response by scoring the tuples in terms of their compatibility with the CSKB. To evaluate ACCENT, we construct the first public event commonsense evaluation dataset for open-domain dialogues.Our experiments show that ACCENT is an efficient metric for event commonsense evaluation, which achieves higher correlations with human judgments than existing baselines.
Ghazarian, Sarik Shao, Yijia Han, Rujun Galstyan, Aram Peng, Nanyun
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems