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.

Example Input: In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. During annotation, we generally relied on categories and guidelines assembled by BBN Technologies for TREC 2002 question answering track. 
 Question: did they use a crowdsourcing platform for manual annotations?
Example Output: Yes-no

Example Input: Our Recurrent +ELMo model uses the language model from BIBREF9 to provide contextualized embeddings to the baseline model outlined above, as recommended by the authors.

Our OpenAI GPT model fine-tunes the 12 layer 768 dimensional uni-directional transformer from BIBREF27 , which has been pre-trained as a language model on the Books corpus BIBREF36 . 
 Question: did they use other pretrained language models besides bert?
Example Output: Yes-no

Example Input: Here I would use Informap algorithm BIBREF12. To make a comparison to this method, I am using CCM and SCA for distance measurement in this experiment, too. UPGMA algorithm would be used accordingly in these two cases. 
 Question: Is the proposed method compared to previous methods?
Example Output:
Yes-no