Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent

Understand

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.