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: Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. The simplest way to generate text embeddings based on term embeddings is to use some kind of aggregation of term embeddings such as an average. This approach was tested for example by BIBREF21 and BIBREF13 . BIBREF22 computed a weighted mean of term embeddings by the construction of a loss function and training weights by the gradient descent method.

Final embeddings for visits are obtained by concatenation of average embeddings calculated separately for the interview and for the medical examination, see Figure FIGREF4 . 
 Question: Which word embeddings do they use to represent medical visits?

A: GloVe concatenation of average embeddings calculated separately for the interview and for the medical examination
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Q: We introduce our proposed diversity, density, and homogeneity metrics with their detailed formulations and key intuitions. 
 Question: Did they propose other metrics?

A: Yes
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Q: the new DMN+ model does not require that supporting facts In addition, we introduce a new input module to represent images. 
 Question: What improvements they did for DMN?

A:
the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. In addition, we introduce a new input module to represent images.
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