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.

Ex Input:
The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2.  
 Question: Do they use dropout?

Ex Output:
Yes-no


Ex Input:
In order to represent individual sentences, we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it. We also use interval segment embeddings to distinguish multiple sentences within a document. This way, document representations are learned hierarchically where lower Transformer layers represent adjacent sentences, while higher layers, in combination with self-attention, represent multi-sentence discourse. Position embeddings in the original Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings that are initialized randomly and fine-tuned with other parameters in the encoder. 
 Question: What is novel about their document-level encoder?

Ex Output:
Extractive


Ex Input:
We quantify the use of these strategies by comparing the airtime debaters devote to talking points . For a side INLINEFORM0 , let the self-coverage INLINEFORM1 be the fraction of content words uttered by INLINEFORM2 in round INLINEFORM3 that are among their own talking points INLINEFORM4 ; and the opponent-coverage INLINEFORM5 be the fraction of its content words covering opposing talking points INLINEFORM6 .  We use all conversational features discussed above. For each side INLINEFORM0 we include INLINEFORM1 , INLINEFORM2 , and their sum. We also use the drop in self-coverage given by subtracting corresponding values for INLINEFORM3 , and the number of discussion points adopted by each side. 
 Question: what aspects of conversation flow do they look at?

Ex Output:

Abstractive