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.

Example Input: In Figure FIGREF15 , we plot the zero-resource German and Japanese test set accuracy as a function of the number of steps taken, with and without adversarial training. The plot shows that the variation in the test accuracy is reduced with adversarial training, which suggests that the cross-lingual performance is more consistent when adversarial training is applied. 
 Question: Do any of the evaluations show that adversarial learning improves performance in at least two different language families?
Example Output: Yes

Example Input: Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ).  AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test.  
 Question: By how much does their method outperform state-of-the-art OOD detection?
Example Output: AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average

Example Input: MTMSN BIBREF4 is the first, and only model so far, that specifically tried to tackle the multi-span questions of DROP.  
 Question: What is the previous model that attempted to tackle multi-span questions as a part of its design?
Example Output:
MTMSN BIBREF4

