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.

Ex 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?

Ex 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 


Ex Input:
As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute). 
 Question: How much better performance of proposed model compared to answer-selection models?

Ex Output:
significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)


Ex Input:
We trained a logistic regression baseline model (line 1 in Table TABREF10 ) using character ngrams and word unigrams using TF*IDF weighting BIBREF13 , to provide a baseline since HAR has no reported results. For the SR and HATE datasets, the authors reported their trained best logistic regression model's results on their respective datasets. 
 Question: what was the baseline?

Ex Output:

logistic regression