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.- Anthology ID:
- 2022.aacl-short.36
- Volume:
- 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:
- November
- Year:
- 2022
- Address:
- Online only
- Venues:
- AACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 295–301
- Language:
- URL:
- https://aclanthology.org/2022.aacl-short.36
- DOI:
- Cite (ACL):
- Meera Hahn and James M. Rehg. 2022. Transformer-based Localization from Embodied Dialog with Large-scale Pre-training. In 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), pages 295–301, Online only. Association for Computational Linguistics.
- Cite (Informal):
- Transformer-based Localization from Embodied Dialog with Large-scale Pre-training (Hahn & Rehg, AACL-IJCNLP 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.aacl-short.36.pdf