@inproceedings{hahn-rehg-2022-transformer,
    title = "Transformer-based Localization from Embodied Dialog with Large-scale Pre-training",
    author = "Hahn, Meera  and
      Rehg, James M.",
    editor = "He, Yulan  and
      Ji, Heng  and
      Li, Sujian  and
      Liu, Yang  and
      Chang, Chua-Hui",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.aacl-short.36/",
    doi = "10.18653/v1/2022.aacl-short.36",
    pages = "295--301",
    abstract = "We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who is attempting to identify the Observer{'}s location, the goal is to predict the Observer{'}s final location in a map. We develop a novel LED-Bert architecture and present an effective pretraining strategy. We show that a graph-based scene representation is more effective than the top-down 2D maps used in prior works. Our approach outperforms previous baselines."
}