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:
The dataset consists of more than $0.3$ million records and has been made available for future research. 
 Question: What is the size of the dataset?

Ex Output:
$0.3$ million records


Ex Input:
We use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter selection 
 Question: Why is a Gaussian process an especially appropriate method for this classification problem?

Ex Output:
avoids the need for expensive cross-validation for hyperparameter selection


Ex Input:
In the first scenario we equip the decoder with an additional morphology table including target-side affixes. We place an attention module on top of the table which is controlled by the decoder. At each step, as the decoder samples a character, it searches the table to find the most relevant information which can enrich its state. Signals sent from the table can be interpreted as additional constraints. In the second scenario we share the decoder between two output channels. The first one samples the target character and the other one predicts the morphological annotation of the character. This multi-tasking approach forces the decoder to send morphology-aware information to the final layer which results in better predictions. In the third scenario we combine these two models.  
 Question: How are the auxiliary signals from the morphology table incorporated in the decoder?

Ex Output:

an additional morphology table including target-side affixes. We inject the decoder with morphological properties of the target language.