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.

Example Input:  We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words). 
 Question: What text-based features are used?
Example Output: language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities)  language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words)

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

Example Input: Inspired by the StackLite tag recommendation task on Kaggle, we build a new benchmark based on the public StackExchange data. We use questions with titles as source, and user-assigned tags as target keyphrases.

Since oftentimes the questions on StackExchange contain less information than in scientific publications, there are fewer keyphrases per data point in StackEx. Furthermore, StackExchange uses a tag recommendation system that suggests topic-relevant tags to users while submitting questions; therefore, we are more likely to see general terminology such as Linux and Java. This characteristic challenges models with respect to their ability to distill major topics of a question rather than selecting specific snippets from the text. 
 Question: How was the StackExchange dataset collected?
Example Output:
they obtained computer science related topics by looking at titles and user-assigned tags