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: We re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification. 
 Question: What is their baseline model?

Answer: the model proposed in BIBREF3


Question: We focus on social media posts from the website Twitter, which are an excellent testing ground for character based models due to the noisy nature of text. Heavy use of slang and abundant misspellings means that there are many orthographically and semantically similar tokens, and special characters such as emojis are also immensely popular and carry useful semantic information. In our moderately sized training dataset of 2 million tweets, there were about 0.92 million unique word types. 
 Question: Does the paper clearly establish that the challenges listed here exist in this dataset and task?

Answer:
Yes