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:
Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference.
Question: How many layers are there in their model?

Ex Output:
two LSTM layers

Ex Input:
Drawing on the concept of variance in mathematics, local variance loss is defined as the reciprocal of its variance expecting the attention model to be able to focus on more salient parts. The standard variance calculation is based on the mean of the distribution. However, as previous work BIBREF15, BIBREF16 mentioned that the median value is more robust to outliers than the mean value, we use the median value to calculate the variance of the attention distribution. Thus, local variance loss can be calculated as:

where $\hat{\cdot }$ is a median operator and $\epsilon $ is utilized to avoid zero in the denominator.
Question: How do they define local variance?

Ex Output:
The reciprocal of the variance of the attention distribution