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.

[Q]: We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual models, capturing crosslingual semantic role patterns. Concretely, when training on parallel sentences, whenever the head words of the arguments are aligned, we add a CLV as a parent of the two corresponding role variables.
Question: Do they add one latent variable for each language pair in their Bayesian model?
[A]: Yes

[Q]: In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events. Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing.
Question: Which of their training domains improves performance the most?
[A]: documents from the CommonCrawl dataset that has the most overlapping n-grams with the question

[Q]: The dataset consists of more than $0.3$ million records and has been made available for future research.
Question: What is the size of the dataset?
[A]:
$0.3$ million records