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:
We draw on a recently released corpus of state speeches delivered during the annual UN General Debate that provides the first dataset of textual output from states that is recorded at regular time-series intervals and includes a sample of all countries that deliver speeches BIBREF11 .  
 Question: Which dataset do they use?

Ex Output:
corpus of state speeches delivered during the annual UN General Debate


Ex Input:
 Last, we evaluate our approaches in 9 commonly used text classification datasets. We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare. 
 Question: What NLP tasks do they consider?

Ex Output:
text classification for themes including sentiment, web-page, science, medical and healthcare


Ex Input:
We presented a new approach for converting between masculine-inflected and feminine-inflected noun phrases in morphologically rich languages. To do this, we introduced a Markov random field with an optional neural parameterization that infers the manner in which a sentence must change to preserve morpho-syntactic agreement when altering the grammatical gender of particular nouns. 
 Question: Which model do they use to convert between masculine-inflected and feminine-inflected sentences?

Ex Output:

Markov random field with an optional neural parameterization