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 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?

Ex Output:
Yes

Ex Input:
We evaluate our method on IWSLT16 German-English (DE-EN) translation in both directions, WMT15 English-German (EN-DE) translation in both directions, and NIST Chinese-to-English (ZH$
ightarrow $EN) translation.
Question: Which languages do they experiment on?

Ex Output:
German English Chinese

Ex Input:
As fig:fit shows, estimated test accuracy is highly correlated with actual test accuracy for various datasets, with worst-case values $\mu <1\%$ and $\sigma <5\%$ . Note that the number of free parameters is small ($||\le 6$) compared to the number of points (4249 model-data configurations), demonstrating the appropriateness of the proposed function for modeling the complex error landscape.
Question: What is proof that proposed functional form approximates well generalization error in practice?

Ex Output:
estimated test accuracy is highly correlated with actual test accuracy for various datasets appropriateness of the proposed function for modeling the complex error landscape