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: An octave convolutional layer BIBREF0 factorizes the output feature maps of a convolutional layer into two groups. The resolution of the low-frequency feature maps is reduced by an octave height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves dividing by $2^t$, where $t=1,2,3$ and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer, and an example with three groups and reductions of one and two octaves is depicted in Fig. FIGREF1.
Question: How is octave convolution concept extended to multiple resolutions and octaves?

A: The resolution of the low-frequency feature maps is reduced by an octave height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves dividing by $2^t$, where $t=1,2,3$ and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer,
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Q: Although no perfect matches exist, we decided upon the Quora duplicate question dataset BIBREF22 as the best match. To study the embeddings, we computed the euclidean distance between the two questions using various embeddings, to study the distance between semantically similar and dissimilar questions.
Question: What existing corpus is used for comparison in these experiments?

A: Quora duplicate question dataset BIBREF22
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Q: Each dataset consists of a collection of records with one QA problem per record. For each record, we include some question text, a context document relevant to the question, a set of candidate solutions, and the correct solution. The context document for each record consists of a list of ranked and scored pseudodocuments relevant to the question. Several baselines rely on the retrieved context to extract the answer to a question. For these, we refer to the fraction of instances for which the correct answer is present in the context as Search Accuracy. Naturally, the search accuracy increases as the context size increases, however at the same time reading performance decreases since the task of extracting the answer becomes harder for longer documents.
Question: Which retrieval system was used for baselines?

A:
The dataset comes with a ranked set of relevant documents. Hence the baselines do not use a retrieval system.
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