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.
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Question: Despite these shortcomings in using WER as a metric of success, the WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD based on the given reading passage and answer. WER can be used for initial analyses that can lead to deeper insights as discussed further below. 
 Question: Why did they choose WER as evaluation metric?

Answer: WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD WER can be used for initial analyses


Question: The two main data sets that we will use to evaluate our architecture are the 2014 i2b2 de-identification challenge data set BIBREF2 and the nursing notes corpus BIBREF3 . 
 Question: Which two datasets is the system tested on?

Answer: 2014 i2b2 de-identification challenge data set BIBREF2 nursing notes corpus BIBREF3


Question: We adopt the mixture of two multilingual translation corpus as our training data: MultiUN BIBREF20 and OpenSubtitles BIBREF21. MultiUN consists of 463,406 official documents in six languages, containing around 300M words for each language. OpenSubtitles is a corpus consisting of movie and TV subtitles, which contains 2.6B sentences over 60 languages. We select four shared languages of the two corpora: English, Spanish, Russian and Chinese. Statistics of the training corpus are shown in Table TABREF14. 
 Question: What multilingual parallel data is used for training proposed model?

Answer:
MultiUN BIBREF20 OpenSubtitles BIBREF21