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: Table TABREF44 shows average results of our automatic and human evaluations. 
 Question: How big is the difference in performance between proposed model and baselines?

Output: Abstractive


Input: Consider Input: For sentence encoding models, we chose a simple one-layer bidirectional LSTM with max pooling (BiLSTM-max) with the hidden size of 600D per direction, used e.g. in InferSent BIBREF17 , and HBMP BIBREF18 . For the other models, we have chosen ESIM BIBREF19 , which includes cross-sentence attention, and KIM BIBREF2 , which has cross-sentence attention and utilizes external knowledge. We also selected two model involving a pre-trained language model, namely ESIM + ELMo BIBREF20 and BERT BIBREF0 . 
 Question: Which models were compared?

Output: Abstractive


Input: Consider Input: First, we propose a class of recurrent-like neural networks for NLP tasks that satisfy the differential equation DISPLAYFORM0

where DISPLAYFORM0

and where INLINEFORM0 and INLINEFORM1 are learned functions. INLINEFORM2 corresponds to traditional RNNs, with INLINEFORM3 . For INLINEFORM4 , this takes the form of RNN cells with either nested internal memories or dependencies that extend temporally beyond the immediately previous hidden state.  
 Question: What novel class of recurrent-like networks is proposed?


Output: Abstractive