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]: The dataset contains a total of 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words. 
 Question: What is the size of this dataset?
[EX A]: 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words

[EX Q]: . Grammatically correct text is needed as the starting point for inserting artificial errors, and we used two different sources: 1) the corrected version of the same FCE training set on which the system is trained (450K tokens), and 2) example sentences extracted from the English Vocabulary Profile (270K tokens). 
 Question: Which languages are explored in this paper?
[EX A]: English 

[EX Q]: We construct the embeddings for each document using BERT models finetuned on MLDoc. We mean-pool each document embedding to create a single vector per document. We then calculate the cosine similarity between the embeddings for the English document and its translation. In Table TABREF21 , we observe that the median cosine similarity increases dramatically with adversarial training, which suggests that the embeddings became more language-independent. 
 Question: How do they quantify alignment between the embeddings of a document and its translation?
[EX A]:
median cosine similarity