TASK DEFINITION: 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.
PROBLEM: Using the distribution of the individual words in a category, we can compile distributions for the entire category, and therefore generate maps for these word categories.  
 Question: Do they build a model to automatically detect demographic, lingustic or psycological dimensons of people?

SOLUTION: No

PROBLEM: Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier. 
 Question: what types of features were used?

SOLUTION: stylometric, lexical, grammatical, and semantic

PROBLEM: The character sets of these 7 languages have little overlap except that (i) they all include common basic Latin alphabet, and (ii) both Hindi and Marathi use Devanagari script. We took the union of 7 character sets therein as the multilingual grapheme set (Section SECREF2), which contained 432 characters. 
 Question: How much of the ASR grapheme set is shared between languages?

SOLUTION:
Little overlap except common basic Latin alphabet and that Hindi and Marathi languages use same script.