In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[Q]: We evaluate our model in a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command. Each environment contains between three and five objects differentiated by their size (small, large), shape (round, square) and color (red, green, blue, yellow, pink), totalling in 20 different objects. Depending on the generated scenario, combinations of these three features are necessary to distinguish the targets from each other, allowing for tasks of varying complexity. 
 Question: What simulations are performed by the authors to validate their approach?
[A]: a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command


[Q]: The observed performance of both the BasicDCGAN and MultitaskDCGAN using 3-classes is comparable to the state-of-the-art, with 49.80% compared to 49.99% reported in BIBREF16  
 Question: What model achieves state of the art performance on this task?
[A]: BIBREF16


[Q]: We train MaLOPa on the concantenation of training sections of all seven languages. 
 Question: How many languages have this parser been tried on?
[A]:
seven