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Transformer-based Localization from Embodied Dialog with Large-scale Pre-training
- Source :
- International Joint Conference on Natural Language Processing (2022)
- Publication Year :
- 2022
-
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.
Details
- Database :
- arXiv
- Journal :
- International Joint Conference on Natural Language Processing (2022)
- Publication Type :
- Report
- Accession number :
- edsarx.2210.04864
- Document Type :
- Working Paper