@inproceedings{lee-goldwasser-2022-towards,
    title = "Towards Explaining Subjective Ground of Individuals on Social Media",
    author = "Lee, Younghun  and
      Goldwasser, Dan",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.findings-emnlp.126/",
    doi = "10.18653/v1/2022.findings-emnlp.126",
    pages = "1752--1766",
    abstract = "Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual{'}s theory of mind and behavior from text is far from being resolved. This research proposes a neural model{---}Subjective Ground Attention{---}that learns subjective grounds of individuals and accounts for their judgments on situations of others posted on social media. Using simple attention modules as well as taking one{'}s previous activities into consideration, we empirically show that our model provides human-readable explanations of an individual{'}s subjective preference in judging social situations. We further qualitatively evaluate the explanations generated by the model and claim that our model learns an individual{'}s subjective orientation towards abstract moral concepts."
}