TASK DEFINITION: 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.
PROBLEM: We test mgru on two well-known datasets, the Penn Treebank and Text8. 
 Question: Which dataset do they train their models on?

SOLUTION: Penn Treebank Text8

PROBLEM: However, no prior work has explored the target of the offensive language, which is important in many scenarios, e.g., when studying hate speech with respect to a specific target. 
 Question: What are the differences between this dataset and pre-existing ones?

SOLUTION: no prior work has explored the target of the offensive language

PROBLEM: Some authors use pretrained embeddings (especially when their data set is too small to train their own embeddings) or try to modify these embeddings and adjust to their set. But the biggest drawback of these approaches is that the corpus for training embeddings can be not related to the specific task where embeddings are utilized. A lot of medical concepts are not contained in well-known embeddings bases. Furthermore, the similarity of words may vary in different contexts. Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. 
 Question: Do they fine-tune the used word embeddings on their medical texts?

SOLUTION:
No