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 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?
Example Output: state-of-the-art PDTB taggers

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: Active learning sharply increases the performance of iteratively trained machine learning models by selectively determining which unlabeled samples should be annotated.  
 Question: What is active learning?
Example Output:
A process of training a model when selected unlabeled samples are annotated on each iteration.