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.

Input: Consider Input: 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?

Output: increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$

Input: Consider Input: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question.
Question: How do they measure performance?

Output: average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values

Input: Consider Input: We would like to thank the organizers of the WASSA-2017 Shared Task on Emotion Intensity, for providing the data, the guidelines and timely support.
Question: what dataset was used?
Output: WASSA-2017 Shared Task on Emotion Intensity