  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.
