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.

[Q]: Just as introduced in sec:introduction, it is intractable to compute similarity exactly, as involving INLINEFORM0 computation. Hence, we consider the monte-carlo approximation: DISPLAYFORM0

where INLINEFORM0 is a list of entity pairs sampled from INLINEFORM1 . We use sequential sampling to gain INLINEFORM6 , which means we first sample INLINEFORM7 given INLINEFORM8 from INLINEFORM9 , and then sample INLINEFORM10 given INLINEFORM11 and INLINEFORM12 from INLINEFORM13 . 
 Question: Which sampling method do they use to approximate similarity between the conditional probability distributions over entity pairs?
[A]: monte-carlo sequential sampling


[Q]: word embedding, input encoder, alignment, aggregation, and prediction 
 Question: what is the architecture of the baseline model?
[A]: word embedding, input encoder, alignment, aggregation, and prediction.


[Q]: This process gives a total of INLINEFORM0 10700 training examples with roughly 2000 to 3000 examples per class, with each speaker having an average of 185 examples. 
 Question: How many instances does their dataset have?
[A]:
10700