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.

Example Input: All documents are segmented into paragraphs and processed at the paragraph level (both training and inference); this is acceptable because we observe that most paragraphs are less than 200 characters. The input sequences are segmented by the BERT tokenizer, with the special [CLS] token inserted at the beginning and the special [SEP] token added at the end. 
 Question: At what text unit/level were documents processed?
Example Output: documents are segmented into paragraphs and processed at the paragraph level

Example Input: 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?
Example Output: 2014 i2b2 de-identification challenge data set BIBREF2 nursing notes corpus BIBREF3

Example Input: In order to understand the latent topics of those #MeToo tweets for college followers, we first utilize Latent Dirichlet Allocation (LDA) to label universal topics demonstrated by the users. Since certain words frequently appear in those #MeToo tweets (e.g., sexual harassment, men, women, story, etc.), we transform our corpus using TF-IDF, a term-weighting scheme that discounts the influence of common terms. 
 Question: How are the topics embedded in the #MeToo tweets extracted?
Example Output:
Using Latent Dirichlet Allocation on TF-IDF transformed from the corpus