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 Q]: 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?
[EX A]: Extractive

[EX Q]: We use word alignments, similarly to other annotation projection work, to project the AMR alignments to the target languages. Our approach depends on an underlying assumption that we make: if a source word is word-aligned to a target word and it is AMR aligned with an AMR node, then the target word is also aligned to that AMR node. Word alignments were generated using fast_align BIBREF10 , while AMR alignments were generated with JAMR BIBREF11 . 
 Question: How is annotation projection done when languages have different word order?
[EX A]: Abstractive

[EX Q]: The main reason is that the datasets only contain a small portion of multi-aspect sentences with different polarities. The distraction of attention will not impact the sentiment prediction much in single-aspect sentences or multi-aspect sentences with the same polarities. 
 Question: Is the model evaluated against the baseline also on single-aspect sentences?
[EX A]:
Yes-no