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 Input:
Four factual attributes pertaining to the 200 colleges are extracted from the U.S. News Statistics, which consists of Undergraduate Enrollment, Male/Female Ratio, Private/Public, and Region (Northeast, South, West, and Midwest).  
 Question: Which major geographical regions are studied?

Ex Output:
Northeast U.S, South U.S., West U.S. and Midwest U.S.


Ex Input:
The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061. 
 Question: Which dimensionality do they use for their embeddings?

Ex Output:
1061


Ex Input:
I also performed several case studies. I obtained document embeddings, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token, and visualized them in two dimensions using INLINEFORM1 -SNE BIBREF24 (all vectors were normalized to unit length). The state of the Union addresses (Figure FIGREF27 ) are embedded almost linearly by year, with a major jump around the New Deal (1930s), and are well separated by party at any given time period.  
 Question: What is an example of a computational social science NLP task?

Ex Output:

Visualization of State of the union addresses