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.

Input: Consider Input: 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?

Output: 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


Input: Consider Input: We first conduct experiments to compare the performance of FlowSeq with strong baseline models, including NAT w/ Fertility BIBREF6, NAT-IR BIBREF7, NAT-REG BIBREF25, LV NAR BIBREF26, CTC Loss BIBREF27, and CMLM BIBREF8. 
 Question: What non autoregressive NMT models are used for comparison?

Output: NAT w/ Fertility NAT-IR NAT-REG LV NAR CTC Loss CMLM


Input: Consider Input: Despite these shortcomings in using WER as a metric of success, the WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD based on the given reading passage and answer. WER can be used for initial analyses that can lead to deeper insights as discussed further below. 
 Question: Why did they choose WER as evaluation metric?


Output: WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD WER can be used for initial analyses