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: The baseline is a hierarchical phrase-based system BIBREF29 with a 4-gram language model, with feature weights tuned using MIRA BIBREF30 . Therefore, an appropriate additional baseline would be to mark translation rules with these indicator functions but without the scores, akin to identifying rules with phrases in them (Baseline + SegOn). 
 Question: Which translation systems do they compare against?

Answer: hierarchical phrase-based system BIBREF29 appropriate additional baseline would be to mark translation rules with these indicator functions but without the scores, akin to identifying rules with phrases in them (Baseline + SegOn)


Question: 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?

Answer: state-of-the-art PDTB taggers


Question: The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question. 
 Question: What do they mean by answer styles?

Answer:
well-formed sentences vs concise answers