Where Are You? Localization from Embodied Dialog
Meera Hahn, Jacob Krantz, Dhruv Batra, Devi Parikh, James Rehg, Stefan Lee, Peter Anderson
Abstract
We present WHERE ARE YOU? (WAY), a dataset of ~6k dialogs in which two humans – an Observer and a Locator – complete a cooperative localization task. The Observer is spawned at random in a 3D environment and can navigate from first-person views while answering questions from the Locator. The Locator must localize the Observer in a detailed top-down map by asking questions and giving instructions. Based on this dataset, we define three challenging tasks: Localization from Embodied Dialog or LED (localizing the Observer from dialog history), Embodied Visual Dialog (modeling the Observer), and Cooperative Localization (modeling both agents). In this paper, we focus on the LED task – providing a strong baseline model with detailed ablations characterizing both dataset biases and the importance of various modeling choices. Our best model achieves 32.7% success at identifying the Observer’s location within 3m in unseen buildings, vs. 70.4% for human Locators.- Anthology ID:
- 2020.emnlp-main.59
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 806–822
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.59
- DOI:
- 10.18653/v1/2020.emnlp-main.59
- Cite (ACL):
- Meera Hahn, Jacob Krantz, Dhruv Batra, Devi Parikh, James Rehg, Stefan Lee, and Peter Anderson. 2020. Where Are You? Localization from Embodied Dialog. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 806–822, Online. Association for Computational Linguistics.
- Cite (Informal):
- Where Are You? Localization from Embodied Dialog (Hahn et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.emnlp-main.59.pdf
- Code
- additional community code
- Data
- Matterport3D