Rethinking the ESP game
The ESP Game was designed to harvest human intelligence to assign labels to images - a task which is still difficult for even the most advanced systems in image processing. However, the ESP Game as it is currently implemented encourages players to assign "obvious" labels, which can be easily predicted given previously assigned labels.
 We present a language model which can assign probabilities to the next label to be added. This model is then used in a program, which plays the ESP game without looking at the image. Even without any use of the actual image, the program manages to agree with the randomly assigned human partner on a label for 69% of all images, and for 81% of images which have at least one "off-limits" term assigned to them.
 We discuss how the scoring system and the design of the ESP game can be improved to encourage users to add less predictable labels, thereby improving the quality of the collected information.