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.

Input: Consider Input: To collect a diverse training dataset, we have randomly sampled 1000 posts each from the subreddits politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 
 Question: what are the topics pulled from Reddit?

Output: politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 


Input: Consider Input: In Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3 . 
 Question: What was their performance on emotion detection?

Output: Answer with content missing: (Table 3) Best author's model B-M average micro f-score is 0.409, 0.459, 0.411 on Affective, Fairy Tales and ISEAR datasets respectively. 


Input: Consider Input: The study of the mathematical structure of grammar has indicated that the fundamental things making up sentences are not the words, but some atomic grammatical types, such as the noun-type and the sentence-type BIBREF23 , BIBREF24 , BIBREF25 . The transitive verb-type is not an atomic grammatical type, but a composite made up of two noun-types and one sentence-type. Hence, particularly interesting here is that atomic doesn't really mean smallest...

On the other hand, just like in particle physics where we have particles and anti-particles, the atomic types include types as well as anti-types. But unlike in particle physics, there are two kinds of anti-types, namely left ones and right ones. This makes language even more non-commutative than quantum theory! 
 Question: Do they break down word meanings into elementary particles as in the standard model of quantum theory?


Output: No