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.
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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: As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute).
Question: How much better performance of proposed model compared to answer-selection models?

Answer: significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)

Question: We measure how contextual a word representation is using three different metrics: self-similarity, intra-sentence similarity, and maximum explainable variance.
Question: What experiments are proposed to test that upper layers produce context-specific embeddings?

Answer:
They measure self-similarity, intra-sentence similarity and maximum explainable variance of the embeddings in the upper layers.