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.

Q: The task presented here was easy and simple to analyze, however, future work may be done on natural language tasks. If these properties hold it might indicate that a new evaluation paradigm for NLP should be pushed; one that emphasizes performance on uncharacteristic (but structurally sound) inputs in addition to the data typically seen in training. 
 Question: Can the findings of this paper be generalized to a general-purpose task?

A: Yes-no
****
Q: In this experiment, we compare our Enitity-GCN against recent prior work on the same task. We present test and development results (when present) for both versions of the dataset in Table 2 . From BIBREF0 , we list an oracle based on human performance as well as two standard reading comprehension models, namely BiDAF BIBREF3 and FastQA BIBREF6 . We also compare against Coref-GRU BIBREF12 , MHPGM BIBREF11 , and Weaver BIBREF10 . Additionally, we include results of MHQA-GRN BIBREF23 , from a recent arXiv preprint describing concurrent work. They jointly train graph neural networks and recurrent encoders. We report single runs of our two best single models and an ensemble one on the unmasked test set (recall that the test set is not publicly available and the task organizers only report unmasked results) as well as both versions of the validation set. 
 Question: What baseline did they compare Entity-GCN to?

A: Abstractive
****
Q: Hashtag prediction for social media has been addressed earlier, for example in BIBREF15 , BIBREF16 . BIBREF15 also use a neural architecture, but compose text embeddings from a lookup table of words. 
 Question: Is this hashtag prediction task an established task, or something new?

A:
Abstractive
****