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:
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?

Ex Output:
Yes

Ex Input:
We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edgesthese may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges).
Question: How did they get relations between mentions?

Ex Output:
Assign a value to the relation based on whether mentions occur in the same document, if mentions are identical, or if mentions are in the same coreference chain.

Ex Input:
As previously mentioned, there is no German hate speech corpus available for our needs, especially not for the very recent topic of the refugee crisis in Europe. We therefore had to compile our own corpus.
Question: What languages are were included in the dataset of hateful content?

Ex Output:
German