In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

Input: Consider Input: The initial analyses of the embedding matrices for both the UK and France revealed that in general, words were grouped by context or influence on the electricity consumption. For instance, we observed that winter words were together and far away from summer ones.  
 Question: Is there any example where geometric property is visible for context similarity between words?

Output: Yes-no


Input: Consider 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?

Output: Abstractive


Input: Consider Input: Below, we explore different ways to back off when the ScRNN predicts UNK (a frequent outcome for rare and unseen words):

Pass-through: word-recognizer passes on the (possibly misspelled) word as is.

Backoff to neutral word: Alternatively, noting that passing $\colorbox {gray!20}{\texttt {UNK}}$ -predicted words through unchanged exposes the downstream model to potentially corrupted text, we consider backing off to a neutral word like `a', which has a similar distribution across classes.

Backoff to background model: We also consider falling back upon a more generic word recognition model trained upon a larger, less-specialized corpus whenever the foreground word recognition model predicts UNK. Figure 1 depicts this scenario pictorially. 
 Question: How do the backoff strategies work?


Output: Abstractive