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.

[EX 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?
[EX A]: a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command

[EX Q]: word embedding, input encoder, alignment, aggregation, and prediction 
 Question: what is the architecture of the baseline model?
[EX A]: word embedding, input encoder, alignment, aggregation, and prediction.

[EX Q]: We validate our approach on the Wikipedia toxic comments dataset BIBREF18 . 
 Question: Which datasets do they use?
[EX A]:
 Wikipedia toxic comments