In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Example Input: Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin.  
 Question: Is all text in this dataset a question, or are there unrelated sentences in between questions?
Example Output: the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences 

Example Input: We present a comparison of the proposed models to existing word embeddings approaches. These are: the Bernoulli embeddings (b-emb) BIBREF1 , continuous bag-of-words (CBOW) BIBREF5 , Distributed Memory version of Paragraph Vector (PV-DM) BIBREF11 and the Global Vectors (GloVe) BIBREF6 model. 
 Question: What word embeddings do they test?
Example Output: Bernoulli embeddings (b-emb) BIBREF1 , continuous bag-of-words (CBOW) BIBREF5 , Distributed Memory version of Paragraph Vector (PV-DM) BIBREF11 and the Global Vectors (GloVe) BIBREF6 model

Example Input: The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining. We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB.  We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T). 
 Question: What was the textual source to which OpenIE was applied?
Example Output:
domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining