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.
--------
Question: 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?

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


Question: Our Pointer-Gen+ARL-SEN model achieves the best performance of 60.8%. This is an absolute improvement of 18.2% over the Pointer-Gen baseline. 
 Question: What is this method improvement over the best performing state-of-the-art?

Answer: absolute improvement of 18.2% over the Pointer-Gen baseline


Question: We use one public dataset Social Honeypot dataset and one self-collected dataset Weibo dataset to validate the effectiveness of our proposed features. Before directly performing the experiments on the employed datasets, we first delete some accounts with few posts in the two employed since the number of tweets is highly indicative of spammers. For the English Honeypot dataset, we remove stopwords, punctuations, non-ASCII words and apply stemming. For the Chinese Weibo dataset, we perform segmentation with "Jieba", a Chinese text segmentation tool. After preprocessing steps, the Weibo dataset contains 2197 legitimate users and 802 spammers, and the honeypot dataset contains 2218 legitimate users and 2947 spammers. 
 Question: What is the benchmark dataset and is its quality high?

Answer:
Social Honeypot dataset (public) and Weibo dataset (self-collected); yes