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.

[EX Q]: Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation.
Question: Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
[EX A]: continuous relaxation to top-k-argmax

[EX Q]: A meta-research study found only 1 in 9 qualitative papers in Human-Computer Interaction reported inter-rater reliability metrics BIBREF31. Another related area are meta-research and methods papers focused on identifying or preventing low-effort responses from crowdworkers sometimes called "spam" or "random" responses, or alternatively "fraudsters" or "cheaters." Rates of "self-agreement" are often used, determining if the same person labels the same item differently at a later stage.
Question: What are the key issues around whether the gold standard data produced in such an annotation is reliable?
[EX A]: only 1 in 9 qualitative papers in Human-Computer Interaction reported inter-rater reliability metrics low-effort responses from crowdworkers

[EX 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?
[EX 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,