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: SVMRank is a modification to SVM that assigns scores to each data point and allows the results to be ranked ( BIBREF26 ). We use SVMRank in the experiments below.
Question: what is the supervised model they developed?

A: SVMRank
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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
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Q: KneserNey smoothing In particular, we compare KneserNey smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today.
Question: what classic language models are mentioned in the paper?

A:
KneserNey smoothing
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