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.
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Question: 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?

Answer: Yes


Question: For MSA, we acquired the diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31. The corpus contains 9.7M tokens with approximately 194K unique surface forms (excluding numbers and punctuation marks). For testing, we used the freely available WikiNews test set BIBREF31, which is composed of 70 MSA WikiNews articles (18,300 tokens) and evenly covers a variety of genres including politics, economics, health, science and technology, sports, arts and culture. For CA, we obtained a large collection of fully diacritized classical texts (2.7M tokens) from a book publisher, and we held-out a small subset of 5,000 sentences (approximately 400k words) for testing. 
 Question: what datasets were used?

Answer: diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31 WikiNews test set BIBREF31  large collection of fully diacritized classical texts (2.7M tokens) from a book publisher


Question: In Table TABREF1, we summarize the quantitative results of the above previous studies. In Table TABREF1, we summarize the quantitative results of the above previous studies. 
 Question: What is the accuracy reported by state-of-the-art methods?

Answer:
Answer with content missing: (Table 1)
Previous state-of-the art on same dataset: ResNet50 89% (6 languages), SVM-HMM 70% (4 languages)