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]: We apply attention probes to the task of identifying the existence and type of dependency relation between two words. Our first experiment is an exploratory visualization of how word sense affects context embeddings.  
 Question: What linguistic features were probed for?
[A]: dependency relation between two words word sense


[Q]: Arab-Tweet. For modeling age and gender, we use Arap-Tweet BIBREF19 , which we will refer to as Arab-Tweet. UBC Twitter Gender Dataset. We also develop an in-house Twitter dataset for gender. We manually labeled 1,989 users from each of the 21 Arab countries. The dialect identification model in AraNet is based on our winning system in the MADAR shared task 2 BIBREF20 as described in BIBREF12. The corpus is divided into train, dev and test, and the organizers masked test set labels. We make use of two datasets, the LAMA-DINA dataset from BIBREF22, a Twitter dataset with a combination of gold labels from BIBREF23 and distant supervision labels. The rest of the dataset is only labeled using distant supervision (LAMA-DIST) ($182,605$ tweets) . For more information about the dataset, readers are referred to BIBREF22.  We use the dataset for irony identification on Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24. We collect 15 datasets related to sentiment analysis of Arabic, including MSA and dialects BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34. Table TABREF28 shows all the corpora we use.  
 Question: What datasets are used in training?
[A]: Arap-Tweet BIBREF19  an in-house Twitter dataset for gender the MADAR shared task 2 BIBREF20 the LAMA-DINA dataset from BIBREF22 LAMA-DIST Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24 BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34


[Q]: Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly, our GPU trained CBOW vectors (using the experimental settings found below) in 3568 seconds, whereas training CP-S and JCP-S took 6786 and 8686 seconds respectively. 
 Question: Do they measure computation time of their factorizations compared to other word embeddings?
[A]:
Yes