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:
As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words. In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words.
Question: How do they determine which words are informative?

Ex Output:
Informative are those that will not be suppressed by regularization performed.

Ex Input:
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?

Ex Output:
No

Ex Input:
Training was accomplished using the Kaldi Babel receipe using 198 hours of data in 10 languages (Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese) from GlobalPhone.
Question: What languages are considered?

Ex Output:
Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese