instruction:
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.
question:
We use the following cheap ways to generate pseudo-source texts:

copy: in this setting, the source side is a mere copy of the target-side data. copy-marked: another way to integrate copies without having to deal with OOVs is to augment the source vocabulary with a copy of the target vocabulary.  copy-dummies: instead of using actual copies, we replace each word with “dummy” tokens.  
 Question: what data simulation techniques were introduced?
answer:
Extractive


question:
We collect human judgments on Amazon Mechanical Turk via ParlAI BIBREF18. 
 Question: Do they use crowdsourcing to collect human judgements?
answer:
Yes-no


question:
The performance on discriminating between offensive (OFF) and non-offensive (NOT) posts is reported in Table TABREF18 . We can see that all systems perform significantly better than chance, with the neural models being substantially better than the SVM. The CNN outperforms the RNN model, achieving a macro-F1 score of 0.80. The CNN system achieved higher performance in this experiment compared to the BiLSTM, with a macro-F1 score of 0.69. All systems performed better at identifying target and threats (TIN) than untargeted offenses (UNT). 
 Question: What is the best performing model?
answer:

Extractive