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.

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.  
 Question: How big is QA-CTS task dataset?
17,833 sentences, 826,987 characters and 2,714 question-answer pairs

 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

Results are shown in Table TABREF11 . Results show that incorporating self-attention mechanism in the encoder is beneficial for most tasks. However, original models were better in some tasks (CR, MPQA, MRPC), suggesting that self-attention mechanism could sometimes introduce noise in sentence features. Overall, utilizing self-attentive sentence representation further improves performances in 5 out of 8 tasks. 
 Question: How much better performing is the proposed method over the baselines?

original models were better in some tasks (CR, MPQA, MRPC) utilizing self-attentive sentence representation further improves performances in 5 out of 8 tasks