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.
--------
Question: 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?

Answer: 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


Question: Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings. 
 Question: How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?

Answer: our Open model achieves more than 3 points of f1-score than the state-of-the-art result


Question: Frege promoted what we could call sentence holism: “Only in the context of a sentence does a word have a meaning.” BIBREF10 
 Question: What does Frege's holistic and functional approach to meaning states?

Answer:
Only in the context of a sentence does a word have a meaning.