@inproceedings{li-harrison-2021-error,
    title = "Error Causal inference for Multi-Fusion models",
    author = "Li, Chengxi  and
      Harrison, Brent",
    editor = "{Xin}  and
      Hu, Ronghang  and
      Hudson, Drew  and
      Fu, Tsu-Jui  and
      Rohrbach, Marcus  and
      Fried, Daniel",
    booktitle = "Proceedings of the Second Workshop on Advances in Language and Vision Research",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.alvr-1.2",
    doi = "10.18653/v1/2021.alvr-1.2",
    pages = "11--15",
    abstract = "In this paper, we propose an error causal inference method that could be used for finding dominant features for a faulty instance under a well-trained multi-modality input model, which could apply to any testing instance. We evaluate our method using a well-trained multi-modalities stylish caption generation model and find those causal inferences that could provide us the insights for next step optimization.",
}