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 Input:
For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to.
Question: Along which dimension do the semantically related words take larger values?

Ex Output:
dimension corresponding to the concept that the particular word belongs to

Ex Input:
Our model contains five independent decoders, one for each image in the sequence. This allows each decoder to learn a specific language model for each position of the sequence.
Question: Do the decoder LSTMs all have the same weights?

Ex Output:
No

Ex Input:
We explore an architecture based on a stack of dilated convolution layers, effectively operating on a broader scale than with standard convolutions while limiting model size. Standard convolutional networks cannot capture long temporal patterns with reasonably small models due to the increase in computational cost yielded by larger receptive fields. Dilated convolutions skip some input values so that the convolution kernel is applied over a larger area than its own. The network therefore operates on a larger scale, without the downside of increasing the number of parameters.
Question: What are dilated convolutions?

Ex Output:
Similar to standard convolutional networks but instead they skip some input values effectively operating on a broader scale.