In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: As shown in Table TABREF46, consensus dropout fusion improves the score of NDCG by around 1.0 from the score of the joint model while still yielding comparable scores for other metrics. As also shown in Table TABREF46, the ensemble model seems to take the best results from each model.

A: Which method for integration peforms better ensemble or consensus dropout fusion with shared parameters?
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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.

A: Do they add one latent variable for each language pair in their Bayesian model?
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Q: We combine 13k questions gathered from this pipeline with an additional 3k questions with yes/no answers from the NQ training set to reach a total of 16k questions. 

A:
what is the size of BoolQ dataset?
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