  Contrary to most natural language processing research, which makes use of
static datasets, humans learn language interactively, grounded in an
environment. In this work we propose an interactive learning procedure called
Mechanical Turker Descent (MTD) and use it to train agents to execute natural
language commands grounded in a fantasy text adventure game. In MTD, Turkers
compete to train better agents in the short term, and collaborate by sharing
their agents' skills in the long term. This results in a gamified, engaging
experience for the Turkers and a better quality teaching signal for the agents
compared to static datasets, as the Turkers naturally adapt the training data
to the agent's abilities.
