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 Q]:  To be able to employ the trained model, test sets are first translated to English via machine translation and then inference takes place.   For machine translation, Google translation API is used. 
 Question: how did the authors translate the reviews to other languages?
[EX A]: Using Google translation API.

[EX Q]: he first dataset (WIKI) was the same set of 200 sentences from Wikipedia used in BIBREF7 .  The second dataset (SCI) was a set of 220 sentences from the scientific literature. We sourced the sentences from the OA-STM corpus. In total 2247 triples were extracted. In total, 11262 judgements were obtained after running the annotation process. 
 Question: What is the size of the released dataset?
[EX A]: 440 sentences, 2247 triples extracted from those sentences, and 11262 judgements on those triples.

[EX Q]: The vector gate is inspired by BIBREF11 and BIBREF12 , but is different to the former in that the gating mechanism acts upon each dimension of the word and character-level vectors, and different to the latter in that it does not rely on external sources of information for calculating the gating mechanism. 
 Question: Where do they employ feature-wise sigmoid gating?
[EX A]:
gating mechanism acts upon each dimension of the word and character-level vectors