@inproceedings{modarressi-etal-2022-globenc,
    title = "{G}lob{E}nc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers",
    author = "Modarressi, Ali  and
      Fayyaz, Mohsen  and
      Yaghoobzadeh, Yadollah  and
      Pilehvar, Mohammad Taher",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.naacl-main.19/",
    doi = "10.18653/v1/2022.naacl-main.19",
    pages = "258--271"
}