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]: To further improve the performance, we adopt system combination on the decoding lattice level. By combining systems, we can take advantage of the strength of each model that is optimized for different domains.  The best result for vlsp2018 of 4.85% WER is obtained by the combination weights 0.6:0.4 where 0.6 is given to the general language model and 0.4 is given to the conversation one. On the vlsp2019 set, the ratio is change slightly by 0.7:0.3 to deliver the best result of 15.09%. 
 Question: What is the language model combination technique used in the paper?
[EX A]: system combination on the decoding lattice level combination weights

[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]: Our contribution leverages pre-trained word embeddings (GloVe, trained on wikipedia+gigaword corpus), the DepecheMood affective lexicon, and convolutional neural networks. 
 Question: What embeddings do they use?
[EX A]:
GloVe

