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.

We propose a novel problem of relationship recommendation (RSR). Different from the reciprocal recommendation problem on DSNs, our RSR task operates on regular social networks (RSN), estimating long-term and serious relationship compatibility based on social posts such as tweets. 
 Question: Is this a task other people have worked on?
No

This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame.  
 Question: Do they evaluate only on English data?
Yes

As a document classifier we employ a word-based CNN similar to Kim consisting of the following sequence of layers: $ \texttt {Conv} \xrightarrow{} \texttt {ReLU} \xrightarrow{} \texttt {1-Max-Pool} \xrightarrow{} \texttt {FC} \\ $ Future work would include applying LRP to other neural network architectures (e.g. character-based or recurrent models) on further NLP tasks, as well as exploring how relevance information could be taken into account to improve the classifier's training procedure or prediction performance. 
 Question: Do the experiments explore how various architectures and layers contribute towards certain decisions?

No