instruction:
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.
question:
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?
answer:
monte-carlo sequential sampling

question:
A few sample advice rules in English (these are converted to first-order logic format and given as input to our algorithm) are presented in Table TABREF11 . We modified the work of Odom et al. odomAIME15,odomAAAI15 to learn RDNs in the presence of advice. The key idea is to explicitly represent advice in calculating gradients.
Question: How do they incorporate human advice?
answer:
by converting human advice to first-order logic format and use as an input to calculate gradient

question:
After using Kytea, Japanese texts are applied LSW algorithm to replace OOV words by their synonyms.
Question: Are synonymous relation taken into account in the Japanese-Vietnamese task?
answer:
Yes