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]: We consider QA PGNet and Multi-decoder QA PGNet with lookup table embedding as baseline models and improve on the baselines with other variations described below. 
 Question: What is the baseline?
[A]: QA PGNet Multi-decoder QA PGNet with lookup table embedding


[Q]: At the end of the annotation process, we have annotated 700 tweets, where 175 tweets are in favor of and 175 tweets are against Target-1, and similarly 175 tweets are in favor of and 175 are against Target-2.  
 Question: How many tweets did they collect?
[A]:
700