TASK DEFINITION: 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.
PROBLEM: For the future work, we plan to solve the triples with multiple entities as the second entity, which is excluded from problem scope in this paper. The input of QA4IE is a document $D$ with an existing knowledge base $K$ and the output is a set of relation triples $R = \lbrace e_i, r_{ij}, e_j\rbrace $ in $D$ where $e_i$ and $e_j$ are two individual entities and $r_{ij}$ is their relation. 
 Question: Can this approach model n-ary relations?

SOLUTION: Yes-no

PROBLEM: This allows Macaw to support multi-modal interactions, such as text, speech, image, click, etc. 
 Question: What modalities are supported by Macaw?

SOLUTION: Extractive

PROBLEM: To investigate the trade-off between speed and performance, we compare our technique to standard models with and without attention on a Sequence Copy Task of varying length like in BIBREF14 . For this purpose we use 4 large machine translation datasets of WMT'17 on the following language pairs: English-Czech (en-cs, 52M examples), English-German (en-de, 5.9M examples), English-Finish (en-fi, 2.6M examples), and English-Turkish (en-tr, 207,373 examples). 
 Question: Which datasets are used in experiments?

SOLUTION:
Abstractive