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 use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German. 
 Question: what language pairs are explored?
[A]: German-English Turkish-English English-German


[Q]: After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%).  
 Question: what is the size of their dataset?
[A]: 13,939


[Q]: 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?
[A]:
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


