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]: 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 A]: Yes

[EX Q]: We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks. 
 Question: What normalization techniques are mentioned?
[EX A]: FBanks with cepstral mean normalization (CMN) variance with mean normalization (CMVN)

[EX Q]: The policies trained with the NUS achieved an average success rate (SR) of 94.0% and of 96.6% when tested on the ABUS and the NUS, respectively. 
 Question: by how much did nus outperform abus?
[EX A]:
Average success rate is higher by 2.6 percent points.