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]: 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?
[A]: 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words


[Q]: We design the word-level refine decoder because this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences. 
 Question: Why masking words in the decoder is helpful?
[A]: ecause this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.


[Q]:  In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8. As the first step, we focus on building a bilingual language model (LM) of English and a target language. Starting from a pre-trained English LM, we learn the target language specific parameters (i.e., word embeddings), while keeping the encoder layers of the pre-trained English LM fixed. We then fine-tune both English and target model to obtain the bilingual LM.  
 Question: How is the model transferred to other languages?
[A]:
Build a bilingual language model,   learn the target language specific parameters starting from a pretrained English LM , fine-tune both English and target model to obtain the bilingual LM.