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.

Ex Input:
Experimental Setup 
 Question: what english datasets were used?

Ex Output:
Answer with content missing: (Data section) Penn Treebank (PTB)


Ex Input:
The results in Table TABREF7 show that SRCC performs much better in knowledge extraction. The two documents' contents contain the same idea expressed by terms in a different order that John had asked Mary to marry him before she left. It is obvious that cosine similarity cannot recognize this association, but SRCC has successfully recognized it and produced a similarity value of -0.285714. 
 Question: How do they evaluate knowledge extraction performance?

Ex Output:
SRCC


Ex Input:
Multi-task Pairwise Neural Ranking
We propose a multi-task pairwise neural ranking approach to better incorporate and distinguish the relative order between the candidate segmentations of a given hashtag. Our model adapts to address single- and multi-token hashtags differently via a multi-task learning strategy without requiring additional annotations. In this section, we describe the task setup and three variants of pairwise neural ranking models (Figure FIGREF11 ). Pairwise Neural Ranking Model Margin Ranking (MR) Loss Adaptive Multi-task Learning 
 Question: What set of approaches to hashtag segmentation are proposed?

Ex Output:

Adaptive Multi-task Learning
 Margin Ranking (MR) Loss
 Pairwise Neural Ranking Model