  Can we develop visually grounded dialog agents that can efficiently adapt to
new tasks without forgetting how to talk to people? Such agents could leverage
a larger variety of existing data to generalize to new tasks, minimizing
expensive data collection and annotation. In this work, we study a setting we
call "Dialog without Dialog", which requires agents to develop visually
grounded dialog models that can adapt to new tasks without language level
supervision. By factorizing intention and language, our model minimizes
linguistic drift after fine-tuning for new tasks. We present qualitative
results, automated metrics, and human studies that all show our model can adapt
to new tasks and maintain language quality. Baselines either fail to perform
well at new tasks or experience language drift, becoming unintelligible to
humans. Code has been made available at
https://github.com/mcogswell/dialog_without_dialog

Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language