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.

Input: Consider 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?

Output: Yes


Input: Consider Input: We first conduct experiments to compare the performance of FlowSeq with strong baseline models, including NAT w/ Fertility BIBREF6, NAT-IR BIBREF7, NAT-REG BIBREF25, LV NAR BIBREF26, CTC Loss BIBREF27, and CMLM BIBREF8. 
 Question: What non autoregressive NMT models are used for comparison?

Output: NAT w/ Fertility NAT-IR NAT-REG LV NAR CTC Loss CMLM


Input: Consider Input: In this work, we look at a wide range of attributes and report prediction results on 62 demographic attributes. 
 Question: How many demographic attributes they try to predict?


Output: 62