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.

We have shown that transformer-based encoders together with GRU models provide the best performance for question representation. Notably, we demonstrated that using pre-trained text representations provide consistent performance improvements across several hyper-parameter configurations. We have also shown that using an object detector fine-tuned with external data provides large improvements in accuracy. Moreover, we have shown that attention mechanisms are paramount for learning top performing networks, once they allow producing question-aware image representations that are capable of encoding spatial relations. It became clear that Top-Down is the preferred attention method, given its results with ReLU activation. 
 Question: What components are identified as core components for training VQA models?
pre-trained text representations transformer-based encoders together with GRU models attention mechanisms are paramount for learning top performing networks Top-Down is the preferred attention method

Our study focused primarily on English tweets, since this was the language of our diagnostic training sample. 
 Question: Do the authors report results only on English datasets?
Yes

Figure FIGREF18 shows the confusion matrices of labels between annotators as heatmaps. 
 Question: How is the annotation experiment evaluated?

confusion matrices of labels between annotators