instruction:
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:
They can answer each question with either 'yes', 'rather yes', 'rather no', or 'no'. They can supplement each answer with a comment of at most 500 characters.
Question: What annotations are present in dataset?
answer:
answer each question with either 'yes', 'rather yes', 'rather no', or 'no'. can supplement each answer with a comment of at most 500 characters

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:
We construct the embeddings for each document using BERT models finetuned on MLDoc. We mean-pool each document embedding to create a single vector per document. We then calculate the cosine similarity between the embeddings for the English document and its translation. In Table TABREF21 , we observe that the median cosine similarity increases dramatically with adversarial training, which suggests that the embeddings became more language-independent.
Question: How do they quantify alignment between the embeddings of a document and its translation?
answer:
median cosine similarity