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 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?
[EX A]: performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks

[EX Q]: In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8.  
 Question: How much training data from the non-English language is used by the system?
[EX A]: No data. Pretrained model is used.

[EX Q]: Our analysis shows that attention models traditional alignment in some cases more closely while it captures information beyond alignment in others. For instance, attention agrees with traditional alignments to a high degree in the case of nouns. However, it captures other information rather than only the translational equivalent in the case of verbs. One can see that attention loss varies substantially across different POS tags. In particular, we focus on the cases of NOUN and VERB which are the most frequent POS tags in the dataset. As shown, the attention of NOUN is the closest to alignments on average. But the average attention loss for VERB is almost two times larger than the loss for NOUN. 
 Question: What useful information does attention capture?
[EX A]:
it captures other information rather than only the translational equivalent in the case of verbs