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.

We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message). 
 Question: What baselines did they consider?
state-of-the-art PDTB taggers

 Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).  
 Question: Which machine learning models are used?
RNNs CNNs Naive Bayes with Laplace Smoothing k-clustering SVM with linear kernel

In this paper, we propose the first large-scale dataset for QA over social media data.   Table TABREF3 gives an example from our TweetQA dataset. The dataset now includes 10,898 articles, 17,794 tweets, and 13,757 crowdsourced question-answer pairs 
 Question: What is the size of this dataset?

13,757