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:
The above pseudocode is agnostic with respect to the choice of fragmentation and environment functions; task-specific choices are described in more detail for each experiment below.

Discussion
Question: Which languages do they test on?

Ex Output:
Answer with content missing: (Applications section) We use Wikipedia articles
in five languages
(Kinyarwanda, Lao, Pashto, Tok Pisin, and a subset of English) as well as the Na dataset of Adams
et al. (2017).
Select:
Kinyarwanda, Lao, Pashto, Tok Pisin, and a subset of English

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:
To test the proposed methods ability to generate unsupervised words, it was necessary to devise a method of measuring word relevance. Topic modeling was used based on the assumption that words found in the same topic are more relevant to one another then words from different topics BIBREF14. The complete 200k headline dataset BIBREF11 was modeled using a Naive Bayes Algorithm BIBREF15 to create a word-category co-occurrence model. The top 200 most relevant words were then found for each category and used to create the topic table SECREF12. It was assumed that each category represented its own unique topic.

The number of relevant output words as a function of the headline's category label were measured, and can be found in figure SECREF4. The results demonstrate that the proposed method could correctly identify new words relevant to the input topic at a signal to noise ratio of 4 to 1.
Question: How do they determine similarity between predicted word and topics?

Ex Output:
number of relevant output words as a function of the headline's category label