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.

[Q]: Although MCDN doesn't obtain the highest precision, it increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$.
Question: What performance did proposed method achieve, how much better is than previous state-of-the-art?
[A]: increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$

[Q]: Compared to the in-context-parsing-based system, the combination with exact string matching yields a gain in recall of over 6%, and the combination with inflectional string matching yields an even bigger gain of almost 8%, at precision losses of 0.6% and 0.8%, respectively.
Question: What compleentary PIE extraction methods are used to increase reliability further?
[A]: exact string matching inflectional string matching

[Q]: Among all three pre-training tasks, SR works slightly better than the other two tasks (i.e., NSG and MDG).
Question: Which of the three pretraining tasks is the most helpful?
[A]:
SR