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]: Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. 
 Question: For how many probe tasks the shallow-syntax-aware contextual embedding perform better than ELMo’s embedding?
[A]: performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks


[Q]: Attention-base translation model We use the system of BIBREF6 , a convolutional sequence to sequence model. 
 Question: Which translation system do they use to translate to English?
[A]: Attention-based translation model with convolution sequence to sequence model


[Q]: 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?
[A]:
SRCC