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.

Example Input: 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?
Example Output: monte-carlo sequential sampling

Example Input: The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 .
Question: Where are the hotel reviews from?
Example Output: TripAdvisor

Example Input: Method ::: Passage Ranking Model
The key component of our framework is the Ranker model, which is provided with a question $q$ and $K$ passages $\mathcal {P} = \lbrace p_1, p_2 ... p_K
brace $ from a pool of candidates, and outputs a chain of selected passages. Method ::: Cooperative Reasoner
To alleviate the noise in the distant supervision signal $\mathcal {C}$, in addition to the conditional selection, we further propose a cooperative Reasoner model, also implemented with the MatchLSTM architecture (see Appendix SECREF6), to predict the linking entity from the selected passages.
Question: What are two models' architectures in proposed solution?
Example Output:
Reasoner model, also implemented with the MatchLSTM architecture Ranker model