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.
One example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Solution is here: No
Explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Now, solve this: 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?
Solution:
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,